Impact Magazine Spring 2022

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THE OPERATIONAL RESEARCH SOCIETY

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E D I TO R I A L Last November, the UK hosted COP26 in Glasgow where there was a push for greater international commitment in the fight against climate change. In a report published at the end of February, the Intergovernmental Panel on Climate Change showed that the impacts of the climate crisis are much worse than predicted and said that governments must act more urgently to adapt to them or face global disaster. This issue of Impact focusses on the contribution Operational Research (O.R.) can make to this battle. The cover features one of the dramatic demonstrations of the effects of climate change: the melting polar ice cap. If you look closely in the following pages, you will find a brief reference to the Arctic.

In this issue of Impact, two consultancies, ORTEC and DecisionBrain, report on projects to reduce CO2 emissions, and we read about the transition to electric buses in Rotterdam. The very nature of the climate crisis means that impact from projects may not be seen for several decades. I have therefore included two thought pieces which inform us of where O.R. might be able to make a difference. In one, our regular columnist, Nicola Morrill, addresses decision-makers directly to explain how they might use O.R. in their organisation to help reduce their carbon emissions. Paula Carroll, in the lead article, gives extensive information about the problems we face and their likely impact and indicates how O.R. may be able to support work to reduce their effects. She concludes that the ‘O.R. community would welcome the opportunity to contribute their valuable expertise on problem structuring, modelling and solving to help meet these challenges’.

I hope you enjoy reading these, and the other articles, which show how O.R. and analytics have made, and can make, an impact. All issues are available at https://issuu. com/orsimpact. Please subscribe to this free magazine at https://www.theorsociety. com/impact/. And ensure that the printed issues are not single-use, by passing them on rather than binning them – even if it would be in a recycling bin.

Graham Rand

The OR Society is the trading name of the Operational Research Society, which is a registered charity and a company limited by guarantee.

Seymour House, 12 Edward Street, Birmingham, B1 2RX, UK Tel: + 44 (0)121 233 9300, Fax: + 44 (0)121 233 0321 Email: email@theorsociety.com Secretary and General Manager: Gavin Blackett President: Edmund Burke Editor: Graham Rand g.rand@lancaster.ac.uk Associate Editor: James Bleach Print ISSN: 2058-802X Online ISSN: 2058-8038 www.tandfonline.com/timp Published by Taylor & Francis, an Informa business All Taylor and Francis Group journals are printed on paper from renewable sources by accredited partners.

OPERATIONAL RESEARCH AND DECISION ANALYTICS Operational Research (O.R.) is the discipline of applying appropriate analytical methods to help those who run organisations make better decisions. It’s a ‘real world’ discipline with a focus on improving the complex systems and processes that underpin everyone’s daily life – O.R. is an improvement science. For over 70 years, O.R. has focussed on supporting decision making in a wide range of organisations. It is a major contributor to the development of decision analytics, which has come to prominence because of the availability of big data. Work under the O.R. label continues, though some prefer names such as business analysis, decision analysis, analytics or management science. Whatever the name, O.R. analysts seek to work in partnership with managers and decision makers to achieve desirable outcomes that are informed and evidence-based. As the world has become more complex, problems tougher to solve using gut-feel alone, and computers become increasingly powerful, O.R. continues to develop new techniques to guide decision-making. The methods used are typically quantitative, tempered with problem structuring methods to resolve problems that have multiple stakeholders and conflicting objectives.

Impact aims to encourage further use of O.R. by demonstrating the value of these techniques in every kind of organisation – large and small, private and public, for-profit and not-for-profit. To find out more about how decision analytics could help your organisation make more informed decisions see https://www.theorsociety.com/about-or/or-in-business/. O.R. is the home to the science + art of problem solving.

CO N T E N T S 7

OPPORTUNITIES FOR O.R. TO MITIGATE CLIMATE CHANGE

Paula Carroll reflects on the climate crisis and suggests ways in which O.R. can contribute to the various challenges addressed at COP26

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ELECTRIFYING BUSES IN ROTTERDAM

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USING MATHEMATICAL OPTIMISATION TO ACHIEVE NET POSITIVE COMPANIES

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Pieter van den Berg reports how analysts worked with Rotterdam’s public transport operator to plan for the bus networks being fully electrified by 2030

Seen Elsewhere Analytics making an impact

13 Supporting your efforts on

understanding the potential impact of climate change Nicola Morrill shares several O.R. approaches that can help organisations to reduce their carbon footprint

Goos Kant and Guido de Wit share examples of how organisations are applying optimisation methods to maximise their positive impact and contribute to a better world

31 Universities making an impact

O.R. INCREASES EFFICIENCY AND PRODUCTIVITY WHILST REDUCING CO2 EMISSIONS

42 This Year, Next Year, Sometime,

Giulia Burchi and Alexa Salles report on DecisionBrain’s work with clients to improve their operations while reducing their global footprint

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IMPROVING HOUSING SUPPORT FOR THE VULNERABLE

Brief report of a postgraduate student project

Never?

Geoff Royston considers how our forecasting performance might be improved, reflecting on two books on “the art and science of prediction”

Antuela Tako tells us about using simulation to evaluate services for the frail and elderly in Leicestershire and Rutland

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O.R. CONTRIBUTES TO THE COVID-19 RESPONSE IN THE BRISTOL NHS SYSTEM Richard Wood presents a chronology of how O.R. has been used to address the various problems encountered during the first two years of the COVID-19 pandemic

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NATIONAL GRID CIRCUIT OPTIMISATION

Stefan Sadnicki reports how Copperleaf supports National Grid in optimising asset replacement, refurbishment and maintenance interventions across their network

DISCLAIMER The Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, make every effort to ensure the accuracy of all the information (the “Content”) contained in our publications. However, the Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by the Operational Research Society or our publisher Informa UK Limited, trading as Taylor & Francis Group. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. The Operational Research Society and our publisher Informa UK Limited, trading as Taylor & Francis Group, shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions​

Reusing Articles in this Magazine

All content is published under a Creative Commons Attribution-NonCommercial-NoDerivatives License which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

ELECTRIC VEHICLE CHARGING

As the number of electric vehicles being purchased rapidly increases, so does the task of EV charging on electric utility companies. New research by Owen Wu and Şafak Yücel of Georgetown University, USA, and Yangfang (Helen) Zhou of Singapore Management University, published in the INFORMS journal Manufacturing & Service Operations Management (https://doi. org/10.1287/msom.2021.1019), finds that: 1. Allowing drivers to choose from a menu of prices and charging ­completion times is cheaper for drivers and electric utility companies, and cleaner for the environment.

2. A new electric vehicle charging model can reduce charging cost at public charging stations by 20% and associated emissions by 15% during a summer month.

3. The savings from implementing smart charging can mostly be achieved during peak-demand days.

© Graham Rand

The researchers argue that utilising new business models that promote ‘smart charging’ options can create a win-win situation that strengthens the mass market viability for the entire electric vehicle ecosystem.

O.R. FIGHTING DISEASES THROUGH FRENCH SUPPORT

In the last decade, France has made a major financial commitment to fighting HIV/AIDS, tuberculosis and malaria across francophone countries, many of which are in the developing world.

French project website L’Initiative reports France is maintaining its high level political and financial commitment to multilateral funds, contributing around €700 million a year since 2013. Between 2011 and 2017, €24.5 million of this funding went towards 22 projects and 35 ‘research missions’ that utilised operational research.

L’Initiative says the funding for O.R. ‘made it possible to support projects that seek to enhance the effectiveness of Global Fund grants, and the response to the three pandemics overall, in innovative ways.’ This year, the call is for O.R. projects relating to ‘HIV, HPV and associated cancers’. Benefits may include early detection, targeted treatment and improved cures, as well as better vaccination programmes and – ultimately – effective prevention programmes for some of these deadly diseases.

Find out more here: https://bit. ly/3tofgvW

$1 MILLION ARTIFICIAL INTELLIGENCE PRIZE

Cynthia Rudin, a prominent INFORMS member, has been named the recipient of the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity from the Association for the Advancement of Artificial Intelligence (AAAI). The AAAI Squirrel AI Award is being dubbed the Nobel Prize of AI.

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SEEN ELSEWHERE – DARK GREEN

© Cynthia Rudin

SEEN ELSEWHERE

Rudin, is being honoured for her work in pioneering interpretable and transparent artificial intelligence (AI) systems in real-world deployments, the advocacy for these features in highly sensitive areas such as social justice and medical diagnosis and serving as a role model for researchers and practitioners.

She is a professor of computer science and engineering at Duke University and is the second recipient of the new annual award. Rudin is also a three-time recipient of the INFORMS Innovative Applications in Analytics Award, which recognizes creative and unique applications of analytical techniques.

Rudin has applied her interpretable AI algorithms to several projects, beginning with her work with Con Edison, the energy company that powers New York City, where her team was working to predict power failures. She also collaborated with Massachusetts General Hospital designing a system to predict which patients are most at risk of having seizures after a stroke or other brain injury. Her work with the

SHAPING GLOBAL STANDARDS FOR AI

A UK Government press release (see https://bit.ly/AIGlobalStandards) announces that The Alan Turing Institute, supported by the British Standards Institution and the National Physical Laboratory, will pilot a new UK government initiative to lead in the shaping of global technical standards for AI, as part of a ten-year plan. DCMS Minister for Tech and the Digital Economy Chris Philp said this “marks the first step in delivering our new National AI Strategy and will develop the tools needed so organisations and consumers can benefit from all the opportunities of AI. We want the UK to lead the world in developing AI standards”.

THE STATE OF THE UK’S STATISTICAL SYSTEM 2020/21

A recent review, The state of the UK’s statistical system 2020/21 – Office for Statistics Regulation (statisticsauthority. gov.uk) focussed on the current state of the UK’s statistical system. The authors wish to see the positive momentum of the past year harnessed and improvements made to ensure that statistics and data serve the public good now and in the future. It focuses on five areas found to be central to the statistical system’s response to the COVID-19 pandemic. It argues that the UK’s statistical system should be:

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Responsive and proactive Timely Collaborative Clear and insightful Transparent and trustworthy

RESTORING MISSING DATA

Researchers at the Pusan National University, South Korea, have developed an algorithm to repair missing data in event logs (see https://doi.org/10.1109/ TSC.2021.3118381). Event logs record each activity of a business process, which can then be analysed to improve the processes. Clearly a requirement for this to happen satisfactorily is accurate and complete data. The results obtained demonstrated that the proposed method can significantly improve both the quality of event logs and the overall quality of process mining analysis.

SETTING STANDARDS FOR DATA SCIENCE

Six organisations within the UK have formed an ‘alliance’ with the goal of establishing professional standards for data scientists to adhere to, ‘to ensure an ethical and well-governed approach’ to data science. See https://rss.onlinelibrary.wiley.com/ doi/10.1111/1740-9713.01561 The Alliance for Data Science Professionals argues that standards are needed so that ‘the public, organisations and governments can have confidence in how their data is being used’.

In a statement, the group said: ‘While the skills of data scientists are increasingly in demand, there is currently no professional framework for those working in the field.’ The standards – due to be finalised by the autumn – ‘look to address current issues, such as data breaches, the

misuse of data in modelling and bias in artificial intelligence’.

In addition to the Operational Research Society, the members of the Alliance are the Alan Turing Institute, the British Computer Society, the National Physical Laboratory, the Royal Statistical Society, and the Institute of Mathematics and its Applications.

Standards agreed by the group will be delivered as ‘data science certifications offered by the Alliance members to their professional members’. The standards will also be used as criteria ‘for Alliance members to accredit data science degrees, and data science modules of associated degrees, as contributing to certification’.

Certified data science professionals will also be listed in a ‘single searchable public register’.

Gavin Blackett, the OR Society’s Executive Director, was asked what drew the Society into the Alliance: ‘Many of our members have been describing themselves as doing data science for quite a long time.

We as a society have been keen to demonstrate that O.R. has a role to play in this, and that for data scientists, without a natural home, the OR Society could be one of those potential places for them to see as their professional home. So, we were very pleased when we were asked to come along to the launch meeting for what turned into the Alliance’.

© The OR Society

Cambridge (MA) Police Department developed an algorithm to discover similarities between crimes to determine whether they might be the work of the same criminal. Her work has been highly influential in establishing interpretable machine learning as a mainstream field within AI. This area has shown to be essential for trustworthy and responsible AI systems.

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© Paula Carroll

O P P O R T U N I T I E S F O R O. R .

TO M I T I G AT E C L I M AT E CHANGE PAULA CARROLL

THE INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC) have documented changes in the Earth’s climate in every region and across the whole climate system.

They have concluded that strong and sustained reductions in emissions of carbon dioxide (CO2) and other greenhouse gases can limit the impact of climate change such as rising sea levels and extreme weather events. Operational Research (O.R.) as a discipline emerged to provide decision support during World War 2 – a time of international crises. O.R. offers more than just theory – its ‘real world’ focus as an improvement science supports decision and policy making

to improve the complex systems and processes that underpin society and enable our daily lives. Here, the opportunities to harness O.R. tools and techniques to address the climate change crisis are explored.

According to the IPCC “There’s no going back from some changes in the climate system. However, some changes could be slowed and others could be stopped by limiting warming.” The estimated tipping point is an increase in average temperatures of between 1.5 and 2 °C. National and regional governments design climate and energy action plans to limit global warming to at most 1.5 °C and achieve the ambitions set out in the Conference of the Parties (COP) summits. The UK’s Integrated National Energy and Climate Plan, EU National Energy and Climate Action

IMPACT © 2022 THE AUTHOR

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Plans, and international agencies such as the International Energy Agency “Net Zero by 2050”, set out priorities and roadmaps for the necessary global actions and commitments.

Climate and energy policies aim to meet the commitments of the 2015 COP Paris Agreement to reduce emissions by at least 40% by 2030 compared to 1990, while also allowing economic growth and citizen engagement. Deciding when and what to do is surely a problem that O.R. can address to support the clean energy transition.

Climate and energy policies aim to meet the commitments of the 2015 COP Paris Agreement to reduce emissions by at least 40% by 2030 compared to 1990, while also allowing economic growth and citizen engagement

THE ENERGY TRILEMMA

© World Energy Council

As nations respond to the climate crisis, they must balance the need to provide power to sustain economic growth, with the need to do that in a greener more sustainable way. Policies and changes must also be implemented in an equitable manner. This gives rise to a set of multicriteria problems and sets the challenges for the Energy Trilemma – the challenge of transforming energy systems to be simultaneously secure, sustainable and fair.

The World Energy Trilemma index shown in Figure 1 has been developed to track nations’ progress across these three dimensions. European countries lead the way in balancing

FIGURE 1 WORLD ENERGY COUNCIL ENERGY TRILEMMA INDEX 2020 (taken from https://www.worldenergy.org/publications/entry/ world-energy-trilemma-index-2021)

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the Trilemma and occupy eight of the top 10 places in the 2021 Index. The role of fossil fuels is declining while renewable energy sources play an increasingly important role. 38% of EU electricity in 2020 was generated from renewables, overtaking coal and gas as the main electricity source for the first time. The World Energy Council (WEC) note that the EU Green Deal provides a framework for achieving climate-neutrality goals. Similarly, national policies and post-covid economic stimulus packages include sustainability and decarbonisation targets. The WEC note that Energy Security is being achieved through diversification of generation sources and interconnection, but internationally further pressure to phase-out coal is required.

The UK is ranked fourth in the world for 2021, behind Sweden, Switzerland and Denmark and equal with Finland, with an AAAa rating for Energy Security, Equity, and Sustainability. The UK imports fossil fuel but has decreased its usage of coal as a fuel source.

In the UK, coal consumption has been reduced to less than one-tenth of 1990 levels

BALANCING ENERGY SUPPLY AND DEMAND

Figure 2 shows a Sankey diagram which helps us understand energy balance of supply and demand. In the UK, coal consumption has been reduced to less than one-tenth of 1990 levels as shown in Figure 3. Renewable electricity generation has outpaced fossil fuel generation during four of the last five quarters.

In the last issue of Impact, Bevan Freake https://doi.org /10.1080/2058802X.2021.1885234, describes the carbon calculator provided by the UK’s Department of Business, Energy and Industrial Strategy. It allows users to see the links between electrification of heat and transport on the demand side and increasing renewable energy sources on the supply side. It is available at https://my2050.beis.gov.uk/ Countries with extensive coastlines like the UK and Ireland have taken advantage of their natural geographies and invested heavily in wind power. In 2020, the WEC noted that the UK managed 67 days straight without coalfired generation – the longest period since the industrial revolution – with wind and renewables estimated to contribute ∼36% of energy demand during that period.

Figure 4 shows the increasing penetration of wind power.

The increased penetration of renewables is not without its

FIGURE 2 UK ENERGY FLOW DIAGRAM (Source: https://www.gov.uk/government/collections/uk-energy-in-brief)

FIGURE 3 CHANGE IN UK ENERGY SUPPLY 1990–2020 (Source UK Energy in Brief 2021)

challenges due to their higher variability than traditional energy sources. There is a significant challenge to optimise the use of renewables while adhering to technical constraints of the electricity grid.

In a recent Impact article, https://doi.org/10.1080/20 58802X.2019.1582925, Martina Fischetti described how mathematics and technical knowledge can be combined to create models for offshore wind turbine location and cable

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© World Energy Council FIGURE 4 WIND POWER GENERATION, image used by permission of the World Energy Council. Source: Eurostat, August 2021.

connection problems. Using algorithms to solve these models provides recommended solutions with significant cost savings for such capital-intensive projects.

Brian Clegg also discusses the contribution or O.R. to addressing offshore wind farm problems in an Impact article (https://doi.org/10.1080/2058802X.2017.11964021). He describes the O.R. approach as an academic exercise with a strong focus on the practical benefits, and explains that operational researchers have a broad modelling toolkit.

Where the engineers and geographers have a deep expertise on wind and power systems, O.R. can support better tools for optimisation while collaborating with such subject matter experts.

Turning to the demand side of the energy balance.

Figure 5 shows the changing pattern of end-use in the UK from 1990 to 2020. 2020 was a strange year due to the

Covid pandemic, with decreased economic activity and transport usage and increased domestic demand as many workers worked from home. We can see the significance of the domestic sector in terms of energy demand. We use energy in our homes to provide space and water heat, for cooking and to run appliances and provide lighting.

About 40% of the EU’s energy supply is wasted through inefficiencies, and about 36% of greenhouse gas emissions are produced by buildings. The EU has adopted an efficiency first principle and placed emphasis on improving energy performance in the building sector. According to the Energy Efficiency Indicators Overview Statistics Report published in December 2020 by the International Energy Agency, buildings account for about 40% of the global energy consumption. Across Europe ∼75% of buildings are energy inefficient and most of them (85% − 95%) will still be around by 2050. After “efficiency first” improvements such as insulation and low wattage lighting, smart approaches can be used to help householders understand their usage patterns and their potential to contribute to the green energy transition. Changes to market structures, policies to achieve climate action plans, and the availability of low carbon technologies mean the role of households and buildings is changing. The availability of data, statistical and Machine Learning (open source) software tools, and Smart Grid infrastructure enables Smart Green Homes and creates opportunities to support the transition of householders from passive end user consumers of energy to an active prosumer role where households both produce and consume energy. Prior to the adoption of low carbon technologies, only consumer demand was of interest.

FIGURE 5 UK ENERGY USE BY SECTOR IN MILLION TONNES OF OIL EQUIVALENT, 1990 VERSUS 2020 (Source UK Energy in Brief 2021)

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© Paula Carroll

representative load profiles used to dimension the low voltage distribution network will have to be re-evaluated.

BARRIERS TO THE LOW CARBON TRANSITION – OPPORTUNITIES FOR O.R

FIGURE 6 DAILY ELECTRICITY DEMAND- REPRESENTATIVE LOAD PROFILES Source: Author’s analysis of Irish Smart Meter Data, from the Commission for Energy Regulation (CER). 2012. CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010 dataset.

Figure 6 shows typical daily electricity patterns in Ireland.

These data were gathered during a consumer behaviour trial of smart meters.

About 40% of the EU’s energy supply is wasted through inefficiencies, and about 36% of greenhouse gas emissions are produced by buildings

Bevan Freake notes that the impact on emissions of converting from petrol and diesel to electric vehicles is surprisingly small if the additional electricity demand is not supplied with low carbon renewable electricity generation.

Likewise, the electrification of the heating sector will be successful when the additional electricity needed by the heat pumps comes from a renewable source. In a recent paper (Chesser et al., 2021), the author used statistical modelling to assess the performance of air source heat pumps in retrofitted homes in Ireland and found they meet the definition of renewable heat – that is the ratio of average heat produced to electricity consumers is above an acceptable threshold.

Network operators need to understand the impact of the changing profile of energy demand with increased demand from heat pumps, and electric vehicles, and the potential for self-consumption via distributed renewable energy sources such as solar thermal or photovoltaic systems. The traditional

There are many barriers to achieving a low carbon energy system at technical, statutory, financial, and behavioural levels. A shortfall of information leads to difficulties for optimising measures and policies. Digitalisation of systems, including the power and energy systems through smart meters, may offer opportunities. O.R. and analytics may extract actionable insights from the wealth of data to support the low carbon transformation. Getting the design of policies and support schemes right is an ongoing challenge. Efficient market designs are also essential for mobilising necessary investments for the energy transition. The WEC note that in the UK for example, the 2020 Energy White Paper envisages that by 2050, clean electricity could meet over half of the country’s final energy demand, with increased use of light vehicles and home heating that will require a new approach to how the energy market would be designed, managed and regulated.

Low carbon systems can provide benefits to end-users such as improved local air quality and cost savings. The International Renewable Energy Agency (IRENA) note that advanced computational power and optimisation modelling software, and clear and transparent (wholesale) pricing methodology are two key enabling factors. O.R. has a history of modelling and understanding the capabilities and design requirements of Systems of Systems such as the Energy System.

The road to net-zero greenhouse gas economies includes many challenges to balance the overall energy supply and demand in a sustainable equitable manner. However, all changes face challenges alongside opportunities. O.R. and analytics offer a suite of tools and expertise to model and solve optimisation problems. O.R. has long provided mixed integer linear programming models to support the strategic design of networks, and their day-to-day operational and maintenance. It is interesting to note that George Dantzig developed the simplex method to solve an energy balance Linear Programming model.

The O.R. community can help with recommendations on where in the electricity grid intelligence and renewables should be located to best effect. O.R. can help with design choices from a national generation portfolio mix right down to optimal building low carbon technology design for

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© Paula Carroll FIGURE 7 ROOFTOP PV THE UCD SCHOOL OF BUSINESS, OCTOBER 2021.

building occupants -helping to choose from a mix of low carbon technologies such as Photovoltaic (PV) and battery storage systems. The leading image to this article shows one example of PV panels in an unusual setting, a boat house on the Royal Canal, Dublin. PV panels can be installed on most residential rooftops and on larger institutional buildings such as schools and community buildings, see Figure 7.

O.R. can provide recommendations on when an occupant should consume the electricity, sell it to the grid, or store it in a battery. O.R. optimisation models can include a citizen/ home occupant perspective in addition to the O.R. models traditionally used by transmission and distribution systems operators to centrally manage the energy systems.

O.R. and analytics may extract actionable insights from the wealth of data to support the low carbon transformation

CONCLUSIONS

Managing energy and electricity supply and demand is challenging from a number of perspectives: ensuring continuity of supply; the variability, intermittency and

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distributed nature of renewable energy sources; and the challenge to share in an equitable manner. O.R. tools and techniques such as Mixed Integer Linear Programming are ideal tools for the network and system combinatorial optimisation problems that arise in the energy transition.

Statistical and Machine Learning analytics can help to model and extract insights using the data from the digitalised systems.

The energy transition will be marked by disruption and change. Many multicriteria problems will need to be modelled and solved to incorporate and prioritise the different dimensions of the energy trilemma and the perspectives of stakeholders. As Bevan Freake noted, there are many pathways to low carbon ways of living. The O.R. community would welcome the opportunity to contribute their valuable expertise on problem structuring, modelling and solving to help meet these challenges.

Paula Carroll is an Associate Professor at University College Dublin (UCD). With a background in electrical engineering, she worked in the telecommunications industry before completing a PhD in Network Design using O.R. approaches. She teaches and researches in Business Analytics and Operational Research and has a keen interest in modelling and solving problems that arise in the low carbon energy transition. She is founder and chair of the WISDOM Forum to support and encourage the participation of all genders in O.R. at a European level.

FOR FURTHER READING https://www.gov.uk/government/organisations/ department-for-business-energy-and-industrialstrategy/about/statistics h t t p s : //a s s e t s . p u b l i s h i n g . s e r v i c e . g ov. u k / g o v e r n m e n t / u p l o a d s /s y s t e m / u p l o a d s / attachment_data/file/991649/uk-integratednational-energy-climate-plan-necp-31january-2020.pdf Chesser, M., P. Lyons, P. O’Reilly and P. Carroll (2021).

Air source heat pump in-situ performance. Energy and Buildings, 251: 111365. https://doi.org/10.1016/ j.enbuild.2021.111365

SUPPORTING YOUR EFFORTS ON UNDERSTANDING THE POTENTIAL IMPACT OF CLIMATE CHANGE

get a good understanding of the problem. Typically, wicked problems cannot be solved in the generally accepted sense and the focus should be on how to mitigate their immediate impact. Gaining a deep understanding of the problem, the people involved and how this problem may impact on others is key. Addressing a wicked problem requires an interdisciplinary, iterative approach.

Nicola Morrill

“Complex dynamic systems often generate counterintuitive behaviour” John Sterman COP26 has happened since my last column, and I thought climate change would be an interesting topic to explore. I will briefly outline several ways that O.R. and Analytics can help with understanding the potential impact of climate change. It’s not possible to cover all the possible ways O.R. could help – there are far too many! Instead, I’ve tried to highlight where perhaps lesser-known areas of O.R. can help. In my next article I will explore where more traditional areas of O.R. and Analytics are able to help.

EXPRESSING CLIMATE CHANGE AS A PROBLEM

Firstly, I think it’s helpful to consider the nature of climate change as a problem. In previous columns, I have talked about planning in Volatile, Uncertain, Complex and Ambiguous (VUCA) environments and where improving understanding is key. climate change most certainly creates a VUCA situation.

Aligned with this, climate change is what is referred to as a ‘wicked problem’: generally, a social or cultural problem that’s difficult or impossible to solve—normally because of its complex and interconnected nature. It’s really difficult to

From the Futures and Foresight worlds, climate change is an example of a Megatrend. These are large, transformative global forces that define the future by having far reaching impacts on global society. Megatrends are typically slow to form; persist for a long time (circa. 10-15 years); occur at a global or large scale; and are visible and well known to everyone. They are the underlying forces that drive trends.

Again, the guidance is about understanding megatrends and how they might impact on your world.

So how on earth can O.R. help?

IT’S ALL A BIT COMPLEX

Climate change is made for a systems approach! It is such a complex space that a holistic approach needs to be taken.

It is necessary to think about the link between different elements and what behaviour this might drive over time.

There is a significant amount of material written about climate change and the link to systems thinking. An example is an article in Nature by Berry et al. in 2018, which presented the case for systems thinking for climate change.

See https://www.nature.com/articles/s41558-018-0102-4.

The Systems Thinking for Efficient Energy Planning (STEEP) Project (see https://www.cse.org.uk/projects/ view/1244) is an example of Systems Thinking being used to support low-carbon urban energy master planning in three cities. The work undertaken highlights the complexity of the problem space and the impact different stakeholder

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views can have on a successful outcome. A paper by Freeman and Yearmouth in 2017 (see https://doi.org/10.1016/j. erss.2016.11.009) includes the work which looked at Bristol: subsequently published as an undated ‘miniStern Review,’ by Gouldson and Millward-Hopkins, The Economics of Low Carbon Cities. This reviewed the cost and carbon effectiveness of a wide range of the low carbon options that could be applied in Bristol in households, industry, commerce and transport. (See https://bit.ly/ BristolLowCarbon). There are lots of ‘mini-Stern Reviews’ covering different cities within the UK.

built around spreadsheets, explore the possible scenarios for cutting greenhouse gas emissions by encouraging users to identify and explore different possibilities. The models are used to support understanding with the general public and within schools. For further details please see an article by Bevan Freake in the Autumn 2021 issue of this Magazine.

The O.R. models are being used to aid understanding about a complex problem across a range of stakeholder communities. In these examples the models are being used in a participatory way with the general public.

HOW MIGHT CLIMATE CHANGE UNFOLD SIMULATION & GAMING

Another example of systems approaches being used is The Climate Action Simulation which has a system dynamics model at its heart. This is a simulation-based role-playing game that enables participants to learn for themselves about the response of the climate-energy system to potential policies and actions. It helps improve understanding about the dynamics and interactions of different policy choices. The Climate Action Simulation is framed by the En-ROADS, or Energy Rapid Overview and Decision Support, computer model. Figure 1 is a screenshot of EnROADS, showing the main control panels and graphs of the energy mix and expected global temperature outcomes to 2100. More details are available here: https://www. climateinteractive.org/climate-action-simulation/ My2050 and the MacKay Carbon Calculator, both developed by the UK Government’s Department for Business, Energy & Industrial Strategy (BEIS), are examples of models designed to aid understanding of options for meeting emission targets. The interactive analytical models,

FIGURE 1 SCREENSHOT OF EN-ROADS (Source: https://journals.sagepub. com/doi/full/10.1177/1046878119890643 with permission)

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Climate change could unfold in many different ways and, from a planning point of view, it’s good to be cognisant of this. One commonly used approach in Futures and Foresight to help with this is Scenario Planning. Scenarios are vehicles to explore how the future could unfold and they are not about prediction.

The examples below highlight the range of ways that scenarios can assist from the strategic to the local level.

The Intergovernmental Panel on Climate Change’s (IPCC) Sixth Assessment Report has a number of scenarios: 1.

2.

3.

4.

Taking the green road.

Taking the green road but a little bit slower.

The middle ground.

Countries retreat from international cooperation over climate change.

5. Humanity doesn’t care about climate change and actions are actually making things worse.

It is possible to use these scenarios or others to support your business planning. It is certainly worth contemplating if your current planning is based around one or many of these future possibilities.

Rolls-Royce are an example of one organisation that uses scenarios to improve understanding of the potential impact of climate change on its business. Scenarios are commonly used to assess the resilience of business models and strategy. In this case, Rolls Royce noted that climate change accelerated some of its core risks, identified climate change needed to be treated as a key risk in its own right and the work also identified some business opportunities related to the transition to a low carbon economy. For an insight into the nature of the scenarios used see https://bit. ly/RollsRoyceSustainability.

In 2018, Flynn et al. (see https://doi.org/10.1016/j. envsci.2017.10.012) highlighted that Participatory

about people. Behavioural O.R. is about the use of O.R. methods to model human behaviour in complex settings and the role and impact of behavioural aspects related to the use of O.R. to support problem solving and decision making. An area to look out for.

I have only scratched the surface in this article with some of the areas where O.R. is able to help understand the potential impacts of climate change. I hope you agree O.R. has much to contribute!

SHAPING MY NEXT PIECE FIGURE 2 SECTORS ENGAGED IN PSP IN THE ARCTIC (Reprinted from Environmental Science & Policy, 79, pp 45-53, Flynn et al., Participatory scenario planning and climate change impacts, adaptation and vulnerability research in the Arctic, Copyright (2018), with permission from Elsevier)

Scenario Planning (PSP) approaches are increasingly being used in research on climate change Impacts, Adaptation, and Vulnerability (IAV). This is where the scenarios are created by working directly with the potentially impacted community; sometimes referred to as bottom-up scenario work. The work referred to in their paper undertook a systematic review of participatory scenario work about climate change in the Arctic area. This identified a range of Sectors engaged in bottom-up scenario work related to climate change, which is detailed in the Figure 2.

The three distinct types of scenarios mentioned in this section highlight the breadth of areas where scenario planning can have an impact, and it is, in helping people think through the possible impacts of climate change – whether this is at a national, company, community or individual level.

POWER OF PEOPLE

There is a growing area of O.R. called Behavioural O.R. that could assist. Given that a lot of the change required to achieve the desired outcomes related to climate change are

I plan to use my next article to highlight the way that other areas of O.R. and Analytics support planning around the impact of climate change across different types of organisations.

Beyond this, if there is something, related to O.R., that you would like me to consider for future columns in Impact then please get in touch. The goal is to share the discipline with users/potential users of O.R. by highlighting how it could support ‘business’ challenges they may be facing.

WANT TO LEARN MORE?

The OR Society runs training courses on much of the above if you want to bolster your in-house team. Also, The OR Society Conference is in September each year and is a good opportunity to hear about the application of a broad range of O.R. being applied across a diverse set of challenges.

Maybe this year there will be some papers on climate change.

Nicola Morrill is a Systems Thinking Consultant at Dstl, a certified coach and the current Diversity Champion of the O.R. Society. She writes in a private capacity – all views expressed are her own and all examples are available in the open domain. You can contact her on Nicola.Morrill@googlemail.com

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©Pieter van den Berg

ELECTRIFYING BUSES IN ROTTERDAM PIETER VAN DEN BERG

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IN THE FIGHT AGAINST CLIMATE CHANGE, almost all countries in the world are taking measures to limit CO2 emissions.

A significant sector in this energy transition is the transportation sector, where it is particularly important to replace fossil fuel-powered vehicles with sustainable vehicles. Within public transportation, this mainly concerns bus transport, which is still often carried out with diesel buses. In 2016, the Dutch government and the public transport operators expressed the ambition to replace all diesel buses with sustainably powered buses by 2030 at the latest.

For the public transport operator in Rotterdam, RET, this has led to an enormous operational challenge. It soon became clear that the existing planning would not be feasible due to the limited range and long charging times of the newly purchased electric buses. As a result, a bus can no longer be used continuously throughout the day but will have to be connected to a charger during the day.

In addition, the operational uncertainty has a major impact on the battery level of the buses. For example, unforeseen delays can cause a bus to miss a charging moment and

© Rick Keus

unfavourable weather conditions can lead to higher energy consumption.

Events like this can lead to a situation where a bus has insufficient energy to carry out all scheduled trips. Finally, if the energy used to power the chargers comes from renewable sources such as solar or wind, this will add another layer of uncertainty.

In order to achieve a smooth transition, there has been collaboration between the Rotterdam School of Management, Erasmus University (RSM) and the RET. The project consisted of three phases. First, there was phase 0 in which preparations were made for the introduction of electric buses. Subsequently, the first part of the network was electrified in December of 2019. Finally, the electrification of the second part of the network in the coming years was prepared. At every stage, RSM has contributed to making the transition operationally feasible.

PHASE 0: PREPARATION

In the early stages of the project, RET’s main goal was to quantify the impact of the electrification given the current schedule. This schedule was evaluated by fluctuating a wide range of parameters, including the capacity of the battery, the energy consumption and the capacity of the chargers. To support this analysis, RSM developed a simulation model that evaluates for each set of parameters how many buses would have low battery levels at the end of the day. This analysis clearly

showed that even in the most optimistic scenario with a large battery, low energy consumption and high charging capacity, multiple buses would have insufficient energy to complete all trips in the current schedule. This analysis confirmed that the RET should adapt the planning and take into account the specific characteristics of electric buses in the planning.

This analysis clearly showed that even in the most optimistic scenario with a large battery, low energy consumption and high charging capacity, multiple buses would have insufficient energy to complete all trips in the current schedule Wibout van Ede, head of business operations at RET, said “In the early phases, the simulation tool developed

by RSM gave us the opportunity to easily evaluate the impact of different specifications regarding the battery size, the charging power and the energy consumption. The simulation convincingly showed that significant changes had to be made to our schedule to allow for a feasible electric bus operation and that electric-specific features of the electric buses should be taken into account while designing the trip and bus allocation schedule”.

PHASE 1: NORTHERN NETWORK

From that moment on, the focus was on the first set of buses that would be electrified. This concerned 50 buses that were put into service at the end of 2019. The buses run on seven different lines on the north side of the city of Rotterdam, where they can charge at seven different terminal stations

during the day at fast chargers. During the night, the buses are connected to chargers in the garage with a lower power in order to start the next day with a full battery. In this phase, RET mainly focused on the assignment of trips to the different buses. The schedule should provide sufficient time between rides to make use of the fast chargers.

RSM focused on the development of optimised charging strategies that would strike a balance between a planning that is robust against uncertainty and that avoids unnecessary use of fast chargers. The limited use of the fast chargers is important because it has a major impact on the battery life and on the city’s electricity network. This trade-off was modelled by minimising the number of charging moments during the day under the condition that every bus would have a state-of-charge above a certain threshold at all times. It turned out that the number of charging moments could be more than halved compared to a naive charging strategy in which buses would use all available charging moments.

The question soon arose as to what the impact of delays would be on the state-of-charge of the buses.

If a bus misses a charging moment due to a delay, this can cause the bus to get into trouble later in the day.

To evaluate this impact, we extended the simulation model developed in phase 0 to also be able to analyse the situation with uncertainty. This analysis showed that even with a relatively high state-of-charge threshold in the planning phase, several buses per day could still encounter problems if no real-time adjustments were made. As a result of the uncertainty, it is not clear in advance for which buses measures are required. It is therefore necessary to make real-time adjustments to the charging schedule based on the

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realisations of the uncertainty. For this, we have developed a real-time charging strategy that, based on the optimal offline charging schedule and real-time information, indicates which bus should charge at what time. This strategy appears to provide a robust solution while the number of charging moments can be kept low.

We have developed a realtime charging strategy that, based on the optimal offline charging schedule and realtime information, indicates which bus should charge at what time

PHASE 2: SOUTHERN NETWORK

The first 50 electric buses were put into service in December of 2019.

From that moment on, the focus of the research has shifted to the second set of buses that will be put into service in the coming years. This set of buses will mainly be used on the south side of Rotterdam. Unlike the first set of buses, this set will operate on routes that share a common terminal station. This allows for a situation where all buses charge at the same station. This clustering of the charging activities makes it important to consider the interaction between the lines and to investigate the required number of chargers at this station.

This network structure also offers the opportunity of locally generating renewable energy and using it directly to charge the buses. To this end, the possibility of constructing a solar park at the bus station was evaluated. The uncertainty in the amount of energy generated by this solar park adds an extra layer of complexity to the problem. The question was what the impact of this solar park would be on

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the total energy consumed from the grid and whether the use of renewable energy could be increased by storing part of the generated energy in a battery.

The possibility of constructing a solar park at the bus station was evaluated The results of this analysis show that during summer months, even without the possibility of storage, up to 70% of the energy consumed to charge the buses can come directly from the solar panels. For the winter months this is only 30% due to lower energy generation. In both cases, only a small part of the generated energy cannot be used directly for charging. We also see this when we look at the added value of a battery at the bus station in which part of the unused energy can be stored.

This can lead to an increase in the renewable energy used for charging of about 7%. This is mainly caused by the charging of the battery in the morning before any bus needs charging and discharging the battery at the end of the day after sunset.

Interestingly enough, we see that the added value of the battery is highest in spring and autumn and not in summer.

Due to the high amount of energy generated in summer, there are only few opportunities for the energy in the battery to be used to charge the buses.

In spring and in autumn this is more common due to the limited generation of energy and therefore the battery has a greater added value.

as smooth as possible. Throughout the project, multiple important insights have been obtained. For example, it soon became clear that the impact of electrifying RET’s bus network should not be underestimated and that this requires major changes to their operations. In phase 1, we saw that the impact of delays means that even conservative charging strategies cannot prevent buses from getting into trouble.

It is therefore crucial to apply real-time adjustment to ensure that buses with low battery levels are allocated extra charging moments. Finally, we have seen that a network with a hub for charging the buses is very suitable for using locally generated renewable energy and that in such a situation energy storage has only limited added value.

Writing in May 2020, van Ede, said “the charging schedule optimization model has shown that we could do with fewer charging facilities and that significant improvements can be made to the charging schedule. Using this optimization model, we are able to control various parameters and compare different charging strategies. We are now in the process of implementing the model in our software so that it can be used for our day-to-day planning”.

LESSONS LEARNED

Pieter L. van den Berg is an associate professor of transportation and logistics at the Rotterdam School of Management, Erasmus University. He obtained his MSc in econometrics and operations research from VU University Amsterdam and his PhD in applied mathematics from the Delft University of Technology.

His research focuses on the application of operations research to the logistics of emergency service providers and public transportation.

In this project, RSM and the RET worked closely together to make the implementation of the electric buses

An earlier version of this article appeared in StatOR.

© deepblue4you/iStockPhoto.com

U S I N G M AT H E M AT I C A L O P T I M I S AT I O N TO AC H I E V E N E T P O S I T I V E CO M PA N I E S GOOS KANT AND GUIDO DE WIT

MATHEMATICS AND AI ARE POWERFUL TOOLS to create more efficient and effective organisations.

Investments in optimisation and AI technology have enabled businesses to improve their financial performance.

However, in today’s business context, environmental, social, and governance (ESG) issues are playing a more important role in companies’ decisions.

The focus is shifting from maximising

shareholder value to maximising positive impact. How can mathematics and AI help facilitate this shift?

The focus is shifting from maximising shareholder value to maximising positive impact ORTEC is the world’s leading supplier of mathematical optimisation software and advanced analytics. Our

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mission is to improve the world with our passion for mathematics. Since 1981, ORTEC is a global and leading partner in data-driven decision support.

By leveraging data with our passion for mathematics, we enable many of the best run organisations to optimise their business decisions. Our smart solutions, ranging from business analysis and data analytics to mathematical modelling and optimisation technology, lead to more efficient, adaptive, effective, and sustainable organisations. With 1100 employees across 13 countries, ORTEC globally supports more than 1200 customers to take the best decisions in an ever-changing environment. We are leveraging data and mathematics for a better world. (Discover more at https:// ortec.com/). In this article, we will share examples of how organisations are applying optimisation in an integrated way to maximise their positive impact and contribute to a better world.

STEP 1: IDENTIFY IMPACT OPPORTUNITIES

As with any math problem, the first step is to determine the right objective function. When we speak about impact, this objective function should be defined as an organisation’s desired contribution to the United Nations Sustainable Development

Goals (SDGs). This collection of 17 interlinked, global goals provide a ‘blueprint for peace and prosperity for people and the planet, now and into the future’ (United Nations).

Initially, the SDGs were meant for commitment and participation at the country level. Since then, they have been widely adopted by other organisations as a framework to communicate the impact of their company strategy, both negative (e.g. greenhouse gas emissions, waste generation) and positive (e.g. decent jobs, technological innovation).

To formulate organisational goals as SDG commitments and derive the right objective function for each project, we developed a ‘Project impact methodology’ based on the theory of change. The methodology provides a structured approach to link activities to impact (as shown in Table 1, using a maritime voyage estimation project as an example, and Figure 1).

By understanding how a project is linked to one or more SDGs, we can determine how the underlying mathematical model should be adjusted to maximise the positive contribution to those company goals. In the following section, we explain how this can be achieved by either changing the objective function or by making the best decisions to accelerate transformation.

STEP 2: ADJUSTING THE PROJECT GOALS TO MAXIMISE POSITIVE IMPACT IN THE CURRENT SYSTEM

After identifying impact opportunities, the project goals should be modified to achieve these opportunities. For example, when you implement new routing solutions, you reduce your mileage. This, in turn, results in cost savings and CO2 savings. ORTEC sees an average of 5% CO2 savings when implementing new routing solutions. But often, this is only seen as a positive side effect of cost savings. With more companies setting emission reduction targets, there is a growing need for efficient ways to minimise emissions in current processes. By putting CO2 in the objective function of the model, both economic and environmental benefits can be in balance or even accelerate each other.

ORTEC sees an average of 5% CO2 savings when ­implementing new routing solutions Let’s consider a supply chain network optimisation case in the energy sector. A large energy company with ambitious emission reduction

TABLE 1 PROBLEMS AND OPPORTUNITIES

STEP

QUESTION

EXAMPLE: MARITIME VOYAGE ESTIMATION PROJECT

1. Problems &

What needs

Ships often travel too fast and arrive too early. High speed leads to

Opportunities

improvement?

unnecessarily high fuel consumption.

2. Activities

What is done?

3. Output

What is delivered?

A forecasting engine.

4. Outcome

What is achieved?

Determine the most fuel-efficient engine settings to arrive on time.

5. Impact

Why we do the

Lower costs and emissions through reduced fuel consumption.

project?

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Predict the ship arrival time based on historical data, currently selected route, and weather conditions.

FIGURE 1 QUALITATIVE IMPACT VALUE CHAIN

targets was looking for the smartest way to achieve them.

Their challenge was optimising downstream distribution. Specifically, they had to find the best way to transport products (gasoline, LNG or biofuels) from the production location to a refinery and to end users. To maximise positive impact, we added CO2 emission targets, pricing and local emission legislations to the supply chain network’s design. The model needed to incorporate requirements such as: 1. Making sure all local emission requirements are fulfilled.

2. Taking future CO2 pricing into account when determining the total network cost.

3. Making sure the total network CO2 emissions are in line with the emission reduction goals of the company.

The resulting model can be used to evaluate trade-offs between costs and emissions. It gives the organisation actionable insights to achieve their sustainable ambitions. Since there is a lot of detailed data available on carbon emissions, these kinds of examples are fairly common. With data quality and availability improving across sectors, we expect that more SDG KPIs will be modelled in a similar way in the future.

STEP 3: ACCELERATE TRANSFORMATION

To meet the UN SDGs, many systems need to undergo a transformation.

Optimising the current way of working with different KPIs, as discussed in the previous section, simply won’t be enough. Systemic transformations are taking place all around us: the global energy transition, the transition from a linear to a circular economy, and the transition to mainly plant-based nutrition. These transformations are complex, costly and risky. Data and maths can help organisations make the right decisions.

The transition to zero emission transport is another example. SDG KPIs are seldom considered in the objective functions of models that deal with questions surrounding this kind of transition. Typically, a decision must be made at a strategic level: should we invest in new technology or a process change? The question at hand is how to achieve this in the most effective way.

Let’s consider a specific case.

An online grocery retailer wants to achieve zero-emission home-delivery distribution in 30 to 40 inner cities by 2025. This retailer currently has a national fleet of about 1500 vans. To meet their goal, at least 20% of their fleet must be composed of electric

vehicles (EVs) by 2025. Yet, today, they own a few.

That said, the company is growing extremely fast due to COVID-19 restrictions and market trends. They are forecasting a fleet size of 2500 vans by 2025. In their projected growth, every other new van must be an EV to achieve their goal. To analyse the right fleet mix, they are now testing various brands and characteristics, like the maximum driving range. What makes this a complex decision for the retailer is that there are no EVs currently on the market which can run for a full day of operation. And there won’t be by 2025.

there are no EVs currently on the market which can run for a full day of operation. And there won’t be by 2025 So, if this retailer wants to meet its ambition, driven by local inner-city legislation, their operational model must change, along with the vans that are being bought. They are applying simulations using routing optimisers to compute the optimal fleet mix, considering that deliveries to 30–40 inner cities must be by an EV, while other areas can be delivered by an EV. The routing plan is followed by a calculation that determines which van must be charged at which moment and at which charging station dock. For the morning shift, there is enough loading time; charging can take place overnight.

The afternoon shift is more challenging.

If there is no feasible charging schedule, they must consider alternatives. For example: • Using more EVs, which can be loaded overnight or increasing the charging capacity.

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© Photo by Lucas van Oort on Unsplash

• Accepting fewer orders in the ­corresponding 30–40 inner cities for the afternoon shifts.

There are many practical bottlenecks in this mathematical puzzle: EV availability, the available electricity power provided by the national grid, and whether the company can generate its own power. These complex variables can only be considered with an integrated model.

ORTEC is applying advanced and unconventional techniques to support customers in reaching their SDG goals

ORTEC’S IMPACT

AI and maths are powerful tools to improve the world. ORTEC is applying advanced and unconventional techniques to support customers in reaching their SDG goals. In this article, we covered two cases with a focus on reducing our customers’ CO2 footprint. We measure and publish our contribution in our annual sustainability report (see https://ortec. com/en/news-more/sustainabilityreport-2020).

Sometimes, our work leads to other, surprising results. For example, we work with several pallet pooling companies in Europe. Through our work, these customers reduced the number of wooden pallets and plastic

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crates they use. They now save almost 60,000 wooden pallets every year.

That’s nearly 1.5 million kilograms of wood, which is the equivalent of over 7000 trees. These customers have also reduced their stockpile of plastic crates by 3.5 million. A single stack of this many unfolded crates would be over 400 kilometres high. If you climbed this tower, you could highfive the astronauts in the International Space Station!

They now save almost 60,000 wooden pallets every year Stories like these inspire us to continue contributing to a better world.

Goos Kant, from being a farmer’s son who helped his dad calculate which cows to

keep, to logistics optimisation expert and Managing Partner at ORTEC he has been committed to making an impact since a very young age. Goos specialises in logistic planning and prefers combining academia with a more practical, applied approach.

He has been a professor of logistics optimisation since 2005. He is a regularly an invited speaker at conferences and lectures for executive education programs.

Optimising mathematical models is in his nature, but he is also driven to scout out improvements that cannot be found in models.

Guido de Wit is Practice Lead Sustainability within ORTEC. He is in charge of developing the right propositions to make a positive, sustainable impact with mathematics. Guido has extensive experience as a consultant in the transportation and storage industry and the energy sector.

© Lenny Kuhne/Unsplash; sol/Unsplash; Charanjeet Dhiman/Unsplash

O.R. INCREASES EFFICIENCY AND PRODUCTIVITY WHILST REDUCING CO2 EMISSIONS GIULIA BURCHI AND ALEXA SALLES

APPLIED MATHEMATICS CAN BE USED to solve almost any kind of problem in our daily life. When mathematical optimisation is applied to solve industrial problems the results that can be obtained are very beneficial thanks to the accuracy that can generate a solution. DecisionBrain, a company that develops decision support optimisation solutions, based in Paris, Montpellier, Hong Kong, the United States and Brazil, uses Operational Research techniques every day to help its customers make better decisions and grow their business with advanced analytics solutions. Combining Optimisation and Machine Learning techniques for business variables forecasting, DecisionBrain creates innovative and customisable decision support solutions that drive operational efficiency, increase productivity and generate a significant positive

environmental impact by reducing the carbon footprint.

The solutions help companies make the most of their resources, minimising inefficiency and reducing waste. Depending on the application (manufacturing, mobile workforce, logistics, etc…), their customers have experienced significant reductions in CO2 emissions, energy consumption, material waste. Thanks to the type of solutions that DecisionBrain develops, companies are able to combine significant financial return, in the form of high ROI projects, with an impressive reduction in their operations’ CO2 footprint.

DECISIONBRAIN: SMARTER DECISIONS, BETTER RESULTS

DecisionBrain is able to easily develop customisable solutions for the specific

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1. A development platform, called DB Gene, which provides out-of-thebox approximately 70% of the code needed for an optimisation solution (UI components, user access, data and scenario management, task server). This allows them to easily customise solutions to specific customer needs. DB Gene is also commercialised by IBM within its Data and AI product offering as the new version of IBM Decision Optimization Center (DOC) 2. Industry-specific modules made of general-purpose optimisation engines that can easily be deployed across different use cases within the same industry 3. An optimisation solver: IBM CPLEX The company’s main areas of expertise are Manufacturing and Supply Chain, Logistics, Workforce, and Maintenance.

Focusing on solving problems on sizing, planning, scheduling, and dispatching optimisation across several industries, from Field Services and Pharma Salesforce to Automotive, Transportation, Semiconductors, and Textile manufacturers. The solutions span from real-time to operational, tactical, and strategic planning horizons.

To give a flavour of the type of projects that DecisionBrain develops, there follows three different case studies: Inbound Logistics Optimisation for Toyota, Field Service Scheduling Optimisation for Integral-JLL, and Operational Scheduling and Tactical Planning Optimisation for a Customer Centre.

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INBOUND LOGISTICS OPTIMISATION FOR TOYOTA

Toyota wanted to streamline the logistics from its suppliers to its assembly plants in Thailand, just-insequence and just-in-time, targeting a 2% transportation costs reduction.

With 5 assembly plants and more than 4000 supplier plants, Thailand is the third larger car producer for Toyota after Japan and the US. Due to the very high volume of vehicles produced, the current inbound logistics planning process, mainly performed manually, was very complex and human-intensive.

DecisionBrain used mathematical models to replicate planners’ decisionmaking, reducing planning time from 2.5 days to 1 hour DecisionBrain used mathematical models to replicate planners’ decisionmaking, reducing planning time from 2.5 days to 1 hour. Optimisation techniques were applied to produce more efficient plans, reducing transportation costs while preserving service levels. The solution focused

on optimising order grouping, truck routing, and 3D packing specific to each truck. Results led to over 10% cost reductions, considerably above customer expectations, and also a 10% reduction in CO2 emissions.

The inbound logistics optimisation solution was developed using DecisionBrain’s DB Gene platform and uses IBM CPLEX optimisation libraries. It enabled data scientists and developers to quickly prototype, test, and select the best optimisation strategy.

Planners were part of the process at an early stage, reviewing the solution results as they were produced, thus facilitating their final acceptance. The project was completed in eight months, from business requirements to full deployment. This allowed for a high ROI and a payback time of less than one year.

FIELD SERVICE SCHEDULING OPTIMISATION FOR INTEGRAL-JLL

Integral UK Ltd is the leading Mechanical, Electrical, and Fabric property maintenance business in the UK, providing both Planned Preventative and Reactive Maintenance © Xavier Rabasa/Unsplash

needs of each company and delivers significant ROI thanks not only to the techniques they apply but also to the technology they use. Their technology stack consists of:

© Emmanuel Ikwuegbu/Unsplash

to over 1,600 clients in 60,000 locations. In 2016, Integral was acquired by JLL, making it part of the larger JLL real estate management brand. The challenge was how Integral could take its engineer field service distribution to higher productivity levels with optimisation and dynamic scheduling tools.

The FSO (Field Service Optimization) solution was a platform built using DecisionBrain workforce Dynamic Scheduler. This platform was customised to the needs of the entire Integral field service team (20,000 technicians) and took into account several organisational layers: project executives, planning managers, planners, and field service workers. By blending real-time optimised plans into an intuitive and easy-to-use interface, FSO provided several benefits for both planners and managers: minimised technician travel and idle time, ability to quickly focus on most urgent issues, possibility to easily adapt and adjust plans to unexpected events, seamless collaboration across teams.

The outcome was a 40% productivity increase measured as jobs-per-man per day The outcome was a 40% productivity increase measured as jobsper-man per day, increased service level agreement (SLA) adherence, measured as contractual timing constraints met, with significant improvements for SLAs with 4-hour response time. Without DecisionBrain’s optimisation project, 190 technicians would have traveled about 20% more, (135,505 more miles), meaning there was a reduction in carbon emissions of 35 metric tons.

OPERATIONAL SCHEDULING AND STRATEGICAL-TACTICAL PLANNING OPTIMISATION FOR A CUSTOMER CENTRE

Vivetic is a French company that focuses on delivering outsourcing, customer relations, and call-centre services. It is based in France and Madagascar, employing about 2,000 employees. The company wanted to leverage advanced

analytics to deploy a decision support tool for planning their leave of absences, hiring, and training and for scheduling their day-by-day activities with a granularity of 15 minutes.

They used excel sheets for scheduling, and the process was highly timeconsuming and complex: Vivetic has a large number of customers, and the employees are very specialised. Skills and competencies need to be taken into consideration for the planning. In addition, some Vivetic customers require 24/7 support, thus the employee’s shifts need to be designed to cover this need while respecting French legislation and the employees’ preferences.

DecisionBrain was tasked to develop a customised planning and scheduling tool that could generate optimal plans in minutes. The solution is structured around two modules, one for tactical and strategical planning and the other for operational planning.

1. The strategical and tactical planning module looks up to a year ahead and allows to optimally plan for hiring, upskilling, and leave of absences. It includes the following plans.

• Recruitment plan: this allows doing what-if analysis to define an optimal recruitment plan that takes into consideration the initial training plan for the new hires.

• Upskilling plan: this proposes an optimal upskilling plan that allows to better cover the workload while reducing the need for new hires.

• Leaves of Absences Planning (LoA): this distributes each employee’s Leave of Absences across the planning period to maximise demand coverage and productivity while respecting labour regulations and employees’ preferences.

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© Charanjeet Dhiman/Unsplash

analysis: planners have access to a set of parameters and can easily create alternative scenarios, compare them and select the most relevant. They can also make manual adjustments to the plan and schedule and run a consistency check (compliance with rules) following the changes made.

• High-level Workload Distribution: this designs a high-level workload distribution taking into consideration the employees’ available time and skills.

2. The short-term operational scheduling module looks at the next few days, up to two weeks, and ­optimally assigns the tasks to the employees taking into consideration their skills and competencies, maximising the operational KPIs achievements, and following the tactical plan guidelines.

• Shift Design: this defines each employee’s optimal shift (start and end time, breaks, and days off) while respecting the complex country-specific labour regulations, and taking into account employees’ preferences.

• Activities Scheduling: this defines an optimal 15-minutes schedule of the activities each employee should do in the upcoming days, to maximise demand coverage while respecting employees’ skills and preferences.

Operational schedules and tactical plans can be run in a matter of minutes, allowing planners to test different scenarios and perform what-if

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Results led to a productivity gain of +20% demand coverage (up to 50% in certain cases) compared to manual plans, saving time for planners by creating an optimal plan in 10 minutes instead of days, and improving customer satisfaction Results led to a productivity gain of +20% demand coverage (up to 50% in certain cases) compared to manual plans, saving time for planners by creating an optimal plan in 10 minutes instead of days, and improving customer satisfaction. The company managed to reduce costs by 5% thanks to the improved operational efficiency and the simplification of the HR tasks.

The payback time is estimated to be less than a year.

CONCLUSIONS

DecisionBrain has a track record of successful deployments in several countries across the globe, building and deploying solutions for global leading organisations such as Toyota, European Central Bank, Carhartt, IBM, Daimler, SNCF, and ISS World.

Our solutions help companies make the most of their resources, minimising inefficiency and reducing waste.

Depending on the application, our customers have experienced significant reductions in CO2 emissions, energy consumption, material waste. By covering different planning horizons, from real-time to operational, tactical and strategic, the environmental impact is immediate and sustained for the long term.

Giulia Burchi is Business Analyst at DecisionBrain since 2016. Giulia has experience in Project Management for developing and implementing Workforce Solutions. She is the Product Manager for DecisionBrain Gene, a platform that reduces the effort, time, and risk associated with creating tailored solutions. Since December 2019, this platform has been included in IBM Data and AI offering as an OEM: it’s commercialized as the new version of IBM Decision Optimization Center (DOC). Giulia holds a Master’s Degree in Industrial Engineering from the University of Bologna (Italy).

Alexa Salles is Marketing Manager at ­DecisionBrain. She has experience in developing B2B strategic ­activities, ­producing assets resources (videos, ­brochures, whitepapers, etc.) and ­improving search engine optimisation.

Besides marketing activities, she has also been involved in several projects as a business analyst. Alexa holds an Advanced Master’s Degree in Digital Business ­Strategy from Grenoble Ecole de ­Management (France).

© CDC/Unsplash

I M P R OV I N G H O U S I N G SUPPORT FOR THE VULNERABLE ANTUELA TAKO

UK PUBLIC SECTOR HEALTH AND SOCIAL CARE ORGANISATIONS are under pressure to reduce costs and increase the efficiency and effectiveness of their services. The provision of integrated health and social care services has become a priority for government health policy in the UK and other countries. This involves offering care services to patients in the community or at home, away from hospital. In 2021, the Health Foundation argued that such an approach can achieve

lower health care costs in the longer term and disease prevention. Since 2014, across Leicester, Leicestershire and Rutland (LLR) local authorities, health and social care teams and NHS organisations have been collaborating to transform healthcare services. As part of this initiative, several new community-based services offered to frail and older people were being tested locally.

One example of such service is the Leicestershire’s Lightbulb programme based in Blaby County Council.

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This service delivers housing support solutions such as level-access showers or stairlifts for the frail and elderly in Leicestershire and Rutland. It aims to enable residents to lead independent lives and to stay safe in their homes and prevent falls, which in turn helps reduce the need for hospital beds.

THE CHALLENGE

Leicestershire County Council (LCC) needed an independent evaluation of its planned provision of communitybased services. These are complex multi-agency services that require coordination amongst many partners, including healthcare organisations, housing support providers at county and district council level, as well as the local Clinical Commissioning Group.

These are governed by de-centralised hierarchical structures, with distributed power and knowledge, which is typical of today’s organisations in the health and other industries. There was a requirement to involve both service providers and service users in the evaluation. The expertise of the research team at Loughborough University suited this need. The SIMTEGR8 project, which means SIMulation To Evaluate GREAT care, came about as a result. Other project partners included Leicestershire County Council, Healthwatch Leicester and SIMUL8 Corp. It supported the design and development of eight integrated health and social care services in LLR between 2014 and 2017.

FIGURE 1 STAGES OF THE SIMTEGR8 APPROACH

models used in facilitated workshops with groups of stakeholders. We worked collaboratively with relevant stakeholder groups to evaluate the chosen pilot services in terms of their effectiveness in avoiding emergency admissions and to identify ways in which the patient journey could be improved.

A key part of the evaluation involved running a set of workshops with both service providers and service users (patients), using a computer simulation as a dynamic process map to stimulate discussion about the patient journey and to identify improvements in workflows.

A simulation analyst and a facilitator worked closely with the stakeholder group in workshops. We co-produced simulations of the service which underpinned the analysis of the evaluation. We explored the model results to help the group understand the effectiveness of the service and find ways to improve organisational processes and performance. This approach offers more clarity and transparency in understanding the system at hand and to significantly increase commitment to change within the stakeholder organisation, as they feel an integral part of the OR intervention.

A simulation analyst and a facilitator worked closely with the stakeholder group in workshops

OUR APPROACH

In this project we used participative methodologies, which underpinned the development of simulation

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The approach consists of five main stages, of which three are

facilitated workshops: project briefing, conceptual modelling (workshop 1), model development, service providers (workshop 2) and service users (workshop 3) (see Figure 1).

Undertaking this project was challenging as we had to adapt our expertise to a new and complex setting, that of community-based health and social care services, and to involve a new stakeholder group, service users, in the evaluation. Utilising our previous experience of developing other collaborative approaches PartiSim and SimLean, we used soft systems methodology and lean service improvement concepts to design the facilitation process and to co-produce simulations with the stakeholder group.

For the first time, we incorporated patient input into the evaluation process. This meant that we needed to introduce further simplifications to our models and analysis so technical aspects did not affect service users’ participation.

THE SOLUTION

The model developed shows the flow of patient cases offered different types of services in the new redesigned service pathway, starting from a central hub where they are initially triaged for self-help or sent to the locality. The model represents each locality separately, which the user can select at the start of the simulation. For example, Figure 2 displays a screenshot of the model where the Blaby service is chosen. At locality level an initial housing MOT appointment takes place and then, depending on the type of service

FIGURE 2 THE LIGHTBULB SIMULATION MODEL AS A DYNAMIC PROCESS MAP OF THE LIGHTBULB SERVICE

required, minor home alterations (installation of handrails) or major home adaptations (installing a downstairs bathroom or stair lift), service times and number of visits differ. Services funded via the Disabled Facility Grants (DFGs) follow a separate process, which also require approval by the service. Staffing levels (housing support coordinator, occupational therapist and technical officers) are also depicted depending on the locality chosen.

The model outputs include staff utilisation, as a percentage, for the three types of staff involved in providing services and the number of cases completed (throughput) by type of service and resource. Flexibility is embedded in the model so participants could test the model and the customer journey at different staffing and demand levels. This enabled us to validate the model and its outputs at our workshops, which increased participants’ and our confidence in the model and our analysis.

The model and its results provided evidence to support the business case developed for Lightbulb, while the service operated on a pilot basis between 2015 and 2017. Following the

success of the business case, Lightbulb became fully operational in November 2017.

Analysis based on our simulations identified changes in processes and workforce configuration. Our analysis made it clear to the service providers that Lightbulb needed one point of contact for customers needing support in their homes, rather than the seven that were in place at the beginning of the pilot phase.

These changes were implemented in the real service, which led to a more efficient service, resulting in a reduction in waiting times and in the stages involved. Requests for a level-access shower could be resolved in 13 stages (in 2017), as opposed to 27 previously (2016). A 40% fall in case completion times – down from 42 days to 25 days on average – was also achieved. In addition, the service consistently met the 20-week performance target for household adaptations for disabled facility grants during the period from 2017 to 2019, having taken approximately six to eight months prior to the implementation of the changes.

As a consequence of the redesigned Lightbulb service, a 50% reduction

in service costs per case – £200 versus £400 – was achieved. This led to direct cost savings for the service of £180,000 per year during the 2017-2019 period.

The resources released enabled the service to deliver 37% more cases than projected at the planning stage (20162017).

As a result of the SIMTEGR8 work, a change in working practices was put in place to enable faster hospital discharges. Staff responsible for housing at the district councils worked closely alongside their colleagues in hospitals, which meant that patients could get out of hospital quicker, because they had better housing support. Over the two-year period (2017-2019) 1716 patient discharges in total were supported.

Our analysis helped in achieving improved service satisfaction levels for the frail and elderly in Leicestershire and Rutland. This meant that the service consistently met service users’ expectations, who also reported an improvement in their wellbeing, with the biggest areas of impact being quality of life and mental health.

A 40% fall in case completion times – down from 42 days to 25 days on average – was also achieved Lightbulb also delivered economic benefits to the wider local and national health and care system, through savings of approximately £2.1 million during the period from 2017 to 2019.

Estimated cost savings consist of costs avoided due to reductions in falls, falls call-out conveyances, housing support co-ordinator savings to social care, the hospital housing team in acute and mental health hospitals and disabled facility grants process reduction and the overall costs of the disabled facility grants themselves.

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The then Director of Health and Care Integration at Leicestershire County Council, Cheryl Davenport, stated: ‘The SIMTEGR8 modelling was an essential part of developing the care pathway and the evaluation process during the development phase of this project…

[It] helped prove the concept both operationally and financially. The service has since been fully implemented and commissioned on a recurrent basis over multiple financial years.’

The SIMTEGR8 modelling was an essential part of developing the care pathway and the evaluation process during the development phase of this project

IN CONCLUSION

It has been rewarding to work with a service that makes a difference to our

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local communities, especially for the elderly and frail. We are grateful to our project partners, especially the former Director of Health and Care Integration at Leicestershire County Council for the collaboration and for allowing us to support the redesign of such a great service.

The success of this project provides evidence that the concept of using computer simulations to stimulate discussion with stakeholder groups and to find ways to improve the service collaboratively is effective.

We have had lively discussions with many contributors at our workshops.

Engagement with the models was also high.

Our engagement with service users brought a complementary perspective to the evaluation. It helped the researchers and the evaluation project overall to reach more meaningful conclusions.

In the case of the Lightbulb model,

the participants confirmed that the resulting patient waiting times were acceptable to them.

The combination of quantitative analysis (through simulation) with the qualitative analysis (through facilitation) have been useful to support stakeholder groups in understanding complex services. I hope our example inspires readers to realise the value of using collaborative methodologies to improve their organisational practices and services.

Antuela Tako is a Reader (Associate Professor) in Operational Research at Loughborough University. In her research she uses simulation approaches (discrete-event simulation, system dynamics, agent-based simulation) and problem structuring methods to help organisations improve their processes, primarily in healthcare.

She is a recipient of the OR Society’s 2021 President Medal.

U N I V E R S I T I E S M A K I N G A N I M PAC T EACH YEAR STUDENTS on MSc programmes in analytical subjects at several UK universities spend their last few months undertaking a project, often for an organisation. These projects can make a significant impact. This issue features reports of projects recently carried out at one of our universities: University of Southampton. If you are interested in availing yourself of such an opportunity, please contact the Operational Research Society at email@ theorsociety.com VALUE OF FREE TEXT DATA AND CAUSAL MAPPING Laura Hannula, University of Southampton, MSc Operational Research (part-time)

Wärtsilä is a global leader in innovative technologies and lifecycle solutions for the marine and energy markets, operating in more than 70 countries.

While numerical data is widely and successfully used across the company to support customers, Laura’s dissertation focused on generating actionable insights from less-commonly utilised free text data.

The topic for the dissertation stemmed from the need to better collect and use the innovative ideas and opinions of customer-facing staff in management decision making, without engaging in resource-intensive qualitative interviews. Wärtsilä’s Marine Power Sales team therefore trialled data collection via an ad-hoc staff survey, which became the source data for the exploratory dissertation project in summer 2021.

Laura’s dissertation focused on assessing the benefits and challenges associated with the use of free text data. Her practical work highlighted the immense value of drawing insights from free text comments written by employees of various levels of seniority and using these to establish a development roadmap.

With support from Selin Ahipasaoglu from the University of Southampton, she analysed 660 survey responses using Python, experimenting with methods such as sentiment scoring with a lexical model, word similarity, and clustering.

Particularly for the text pre-processing phase, it was beneficial that Laura had been working for Wärtsilä during her MSc studies. This is because the high prevalence of company-specific jargon in the responses meant many of the outof-box sentiment scoring packages tested failed to capture the true sentiment of the comments.

Secondly, she researched Soft Systems Methodology and Causal Mapping to see if problem structuring could help resolve some of the problems noted in the survey. As a practical demonstration, a causal map with over 200 components was constructed to visualise, and thus help the company move towards solving, the challenge of unclear roles and responsibilities – an issue commonly encountered by large organisations.

Lastly, findings were gathered into a Miro dashboard, enabling stakeholders to interact with the output. This

increased the value of the work, as subject matter experts could easily drilldown on insights and make, and share, their own conclusions.

Fraser Scott, the thesis supervisor and Chief, Project Evolution, at Wärtsilä: "Our operative staff are keen to tell us of ways we can improve and things that don’t quite work optimally, but the challenge is effectively consolidating the many voices and words into an understandable set of actionable insights. Laura’s project enabled that and much more as we are able to not only hear the insights, but understand the feelings, allowing us to focus on the actions that will drive the biggest impacts for our business. In a further step, Laura was able to drill into what appears to be a simple problem: ‘clarity of roles and responsibilities’, to breakdown the multitude of reasons why this issue subsists, despite management efforts."

This is an excellent demonstration of theoretical knowledge obtained from MSc Operational Research studies being applied to a real-life business problem, to create value and actionable insights.

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O.R. CONTRIBUTES TO THE COVID-19 RESPONSE IN THE BRISTOL NHS SYSTEM RICHARD WOOD

COVID-19 HAS CAUSED PROBLEMS for health services at the national, regional and local levels. In response, modelling – now a household term – has played a significant role in influencing government policy, particularly the timing and magnitude of various societal restrictions employed over the last two years. However, the vast majority of this effort has been at the national and regional level, with very little actionable insight available for local healthcare systems, where much of the operational decision making takes place, e.g. how many beds to set aside for incoming COVID-19 demand, how many staff to put on standby, how many elective procedures to postpone.

Time and again, experience has shown that individual systems can fare very differently, even within the same region, with local intricacies – demographics, rurality, employment type – prohibiting the meaningful abstraction of nation-level forecasts of cases and hospitalisations. What local systems need is locally-relevant modelling. And there is more to modelling than epidemiological modelling, with the pandemic raising a vast multitude of challenges whose solutions are best approached through an equally diverse range of modelling and analytical techniques.

In the one million resident Bristol, North Somerset and South Gloucestershire (BNSSG) system, we have called upon various methods

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from the O.R. toolkit to confront an evolving set of problems experienced during COVID-19. This article chronicles such efforts from the start of the pandemic in March 2020 to the time of writing in December 2021.

INTENSIVE CARE CAPACITY

Our first concern was for intensive care, following the Imperial College estimate that COVID-19 demand would be over 30 times maximum capacity were no action taken. A discrete event simulation model of the intensive care admissions process was quickly constructed. The problem was that it is not easy to convert beds to intensive care specification, but if we didn’t then patients could die as a result. In order to help understand the scale of this problem, we used our model to project such ‘capacity-dependent’ deaths under a range of possible surge capacities. This informed local decisions on capacity for both ICU beds and temporary morgue space. Some of the projections – based upon the ‘do nothing’ Imperial College projections – are presented in Figure 1, noting that ultimately no capacitydependent deaths occurred due to the lockdown imposed on 23 March 2020.

The model was later extended from its ‘first-come first-served’ assumption to consider other queue disciplines if ICU capacity were overwhelmed (see Wood et al., 2021a). Results revealed that the greatest saving of aggregate life-years lost would be through reverse triage – an ethically debatable policy in which the right is reserved to prematurely discharge certain patients if the admission of others deemed more likely to benefit from care cannot otherwise be guaranteed. This takes into account survival odds and length of stay, in maximising the benefit from the available resource (i.e. the ICU beds). While, in BNSSG, ICU triage has not been implemented to date, we

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FIGURE 1 PROJECTED CUMULATIVE DEATHS FOR A MAJOR ACUTE HOSPITAL WITHIN BNSSG

came perilously close in January 2021, with results of the modelling informing discussions at our Clinical Cabinet. The work was also recognised by Health Data Research UK’s Impact Committee and has helped support further UKRIfunded studies.

Our first concern was for intensive care, following the Imperial College estimate that COVID-19 demand would be over 30 times maximum capacity were no action taken

LOCAL EPIDEMIOLOGICAL MODELLING

Much of our early work on intensive care made use of patient demand trajectories derived from national studies. Given the aforementioned drawbacks of abstracting nationlevel estimates, misalignment to local conditions at the time, and a lack of control over scenario generation, our attention turned to developing our own admission forecasting model. Thankfully, as of Spring 2020, colleagues at the University of Bristol were already working on an SEIR-type compartmental model

which could be purposed to meet our needs (see Booton et al., 2021).

This led to the formation of the BNSSG Scenario Review Working Group, comprising public health analysts, epidemiologists, modellers and managers from across the system.

Meeting weekly, this group would use the model to refresh the ‘best guess’ predictions of bed occupancy based on the latest information available at the time. An example is provided in Figure 2, for the model run on 10 June 2020 (actual values are represented by black dots).

As well as the acute setting, surges in severe COVID-19 can also pressure downstream community services, given that roughly one in five acute admissions require some form of post-discharge support. To project spot purchase capacity requirements, we developed discrete time simulation models of patient flow to both bedded and home visit community care, using the outputs of the SEIR model as inputs for the demand-related parameters. And another important use of the SEIR model came in early 2021, when we carried out substantial adaptations in order to estimate the longer-term effect of mass vaccination on COVID-19 hospitalisations, and thus to understand the residual

Modelling revealed that the daily throughput being considered by managers was much too high and would result in severe congestion

MODELLING THE ELECTIVE BACKLOG

FIGURE 2 MODEL PREDICTIONS FOR COVID-19 HOSPITALISATIONS WITHIN THE BNSSG SYSTEM

capacity for working through the elective backlog. Projections were later validated with peak bed occupancy, reached in Autumn 2021, residing comfortably within the long run modelled inter-quartile range.

OPTIMISING MASS VACCINATION

We also supported the mass vaccination effort by helping with the design of mass vaccination centres. Again, much of the operational decision making took place at the local level, with little centrally provided information to guide healthcare systems in configuring these new and unfamiliar sites. The sequence of events in a vaccination centre actually represents a quite straightforward queueing process (Figure 3), and so, working with Prof Christos Vasilakis of University of Bath’s Centre for Healthcare Innovation and Improvement (with whom the team does much collaborative work), we were able to deploy our generic healthcare pathway modelling software, PathSimR (https://github.com/nhs-bnssg-analytics/ PathSimR), to consider optimal operational parameters.

In advance of Bristol’s Ashton Gate site opening to the public, modelling revealed that the daily throughput being

considered by managers was much too high and would result in severe congestion (see Wood et al., 2021b). This would not just dissuade future vaccines from attending but would also be bad for the first booked patients, many of whom were elderly and had limited capacity to stand around. Instead, management went with our lower throughput figure, with modelling also influencing other aspects of site design, such as less postvaccination Observation capacity in favour of more queueing space between Clinical Assessment and Vaccination.

Other regional vaccination centres were also modelled, with the work being shortlisted for the OR Society’s 2021 Presidents Medal.

Much press coverage has been given to the size of the NHS waiting list, which has grown by 1.6 m from the start of the pandemic to the time of writing. At the local BNSSG level, we have used a fairly basic computer simulation to model overall waiting list size and referral-to-treatment (RTT) waiting times under various scenarios relating to future demand and capacity.

With much O.R. interest typically in the more granular detail of individual pathways or services, this kind of high-level ‘coarse’ modelling has been somewhat neglected – yet, arguably, modelling at this level better aligns with senior decision making.

We have also applied the same model to national data. In April 2020, following central guidance to postpone all non-urgent planned treatments to make way for COVID-19, we estimated that it would take £14.7bn to recover

FIGURE 3 CONFIGURATION OF BRISTOL’S ASHTON GATE MASS VACCINATION CENTRE

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elective performance in the longer term (see Wood, 2022). Despite being only weeks after the pandemic was declared, this figure has now received support from the Health Foundation’s recent estimate of £12.3–16.8bn and the Government’s own pledge of £14bn.

Lately, we’ve adapted the model to take account of the increased risk of patients leaving the waiting list (reneging) as waiting times get prohibitively large (see Howlett & Wood, 2021), using the enhanced model to obtain long run projections for England. These are presented in Figure 4, assuming the ‘return’ of various proportions of the estimated 7.1 million referrals ‘missed’ during the pandemic.

In April 2020, following central guidance to postpone all non-urgent planned treatments to make way for COVID-19, we estimated that it would take £14.7bn to recover elective performance in the longer term

OTHER INDIRECT IMPACTS OF COVID-19

In BNSSG, we have used various other O.R. techniques over the course of the pandemic. We have performed clustering on the 30,000 strong shielding cohort – using an improved understanding of their various needs to better tailor care and advice (see Kenward et al., 2020).

We have used discrete time simulation to model the effect of COVID-19 on our mental health system. Analysis of survey and activity data has led to the finding that certain patient groups may benefit from continued use of outpatient telehealth. And regressing upon community mobility data has shown that, along with directly related COVID-19

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FIGURE 4 ELECTIVE PERFORMANCE PROJECTIONS FOR ENGLAND

demand, societal restrictions have a significant impact on the volume of nonCOVID-19 urgent care demand as well.

THE CONTRIBUTION OF O.R

The examples given here illustrate just some of the potential ways in which agile use of O.R. can usefully inform local healthcare decision making in highly pressured and uncertain times. Other organisations, such as the Midlands-based Strategy Unit, can count on many more examples during the course of the pandemic.

While much of our work in BNSSG is done under the ‘O.R. banner’, there is much ostensibly-O.R. work happening without overt reference to the discipline – consider, for instance, the similarities between the stock-and-flow setup of

system dynamics and the compartmental epidemiological models used aplenty to influence government policy on lockdowns and other restrictions.

Apart from SEIR models and projecting hospitalisations, there is a golden opportunity for O.R. on the bigger question of optimising healthcare resource allocation more widely – the short- and longer-term costs and benefits of prioritising COVID-19 versus elective and cancer care. As we look to ongoing COVID-19 pressure, most recently through Omicron, health bosses would be indebted for robust analysis of this depth. There is also an opportunity for O.R. to contribute to modelling at the next level up – of balancing health outcomes with the wider macroeconomic impacts of societal restrictions and other government policy.

This kind of constrained multi-objective optimisation is a perfect conceptual fit with techniques from the O.R. toolkit.

Through addressing these important matters in the months and years to come, O.R. will cement its role as a force for good in confronting the many difficult challenges posed by COVID-19.

Richard Wood AFORS is Head of Modelling and Analytics at NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group (BNSSG CCG), which oversees the organisation of healthcare services across a one million resident population in South West England. He has a background in O.R. and has applied various O.R. techniques to a number of settings from mathematical biology to credit risk capital modelling.

He has particular interests in working at the interface of academic theory and practical application.

FOR FURTHER READING Booton, R.D., et al. (2021). Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework. BMJ Open 11: e041536. https://doi.org/10.1136/ bmjopen-2020-041536.

Howlett, N.C., and R.M. Wood (2021). Modelling the recovery of elective waiting lists following COVID-19: scenario projections for England. medRxiv. https://doi.org/10.1101/2021.12.13.21267732.

Kenward, C., A. Pratt, S. Creavin, R. Wood, and J.A. Cooper (2020).

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a crosssectional cohort study. BMJ Open 10: e041370. https://doi.org/10.1136/ bmjopen-2020-041370.

Wood, R.M. (2022). Modelling the impact of COVID-19 on elective waiting times. Journal of Simulation, 16: 101–109. https://doi.org/10.1080/17477778.

2020.1764876.

Wood, R.M., et al. (2021a). The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study. Medical Decision Making 41: 393(407). https://doi.org/10.1177/0272989x21994035.

Wood, R.M., et al. (2021b). Operational research for the safe and effective design of COVID-19 mass vaccination centres. Vaccine 39: 3537–3540. https://doi.org/10.1016/j.vaccine.2021.05.024.

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© Copperleaf

N AT I O N A L G R I D C I R C U I T O P T I M I S AT I O N STEFAN SADNICKI

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NATIONAL GRID PLC is one of the world’s largest investor-owned energy utilities. It operates in the UK and US, supplying gas and electricity to millions of customers and communities. It operates at the heart of the energy system, connecting millions of people safely, reliably and efficiently to the energy they use every day.

National Grid Electricity Transmission (NGET) owns the high-voltage electricity transmission network in England and Wales. It is responsible for ensuring electricity is transported from where it is produced to distribution networks and businesses.

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It facilitates the connection of assets to, and management of assets on, the transmission system.

WHAT IS THE PROBLEM?

An optimal schedule for the interventions on each NGET asset can be computed independently by considering the cost of performing each possible intervention in each year and the monetised risk reduction achieved from the intervention, as illustrated in Figure 1.

However, in scheduling this work, there are several “portfolio” considerations.

© Copperleaf FIGURE 1 FULL LIFE CYCLE STRATEGY FOR ASSETS

The technical challenge is to find the optimal intervention schedule for each asset in the network while considering the benefits of the bundling gains and while honouring the outage and resource constraints Outage Constraints: In order to maintain reliability of the network, the circuit outages must be carefully

coordinated. For example, there may be a set of four circuits where only one of the four circuits can be taken out in one year.

Resource Constraints: In addition, there are resources required to perform each intervention and the interventions must be scheduled in a way such that these resource constraints are met.

The optimal solution is that which provides the greatest value, where the value is defined as the risk mitigated minus the cost.

relied on a planner recognising a potential work bundling opportunity, then building all the resource and outage requirements together into a deliverable package. A further challenge was therefore to capture all the relevant business rules – a ‘modelled’ plan which is theoretically optimal but not truly reflective of the system under consideration would not be fit for purpose.

CHALLENGES

The technical challenge is to find the optimal intervention schedule for each asset in the network while considering the benefits of the bundling gains and while honouring the outage and resource constraints.

Prior to the project, the work schedule was manually created. This

© National Grid

Bundling Gains: In order to perform each intervention, an outage must be taken on the circuit containing the asset and there is a cost associated with these outages. However, because multiple assets belong to the same circuit, an overall reduction in costs and outage time can be achieved by scheduling interventions on different assets in the same circuit at the same time.

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Using the multi-step technique, NGET and Copperleaf were able to run network wide optimisations involving more than 60,000 assets, considering over 5,000 interventions per year, with 50 teams each using up to five different resource type and 100 overlapping boundaries for circuit constraint management

SOLUTION OVERVIEW

In early 2019, NGET started working with Copperleaf, a global decision analytics software provider.

Copperleaf provides decision analytics to companies facing the challenges of managing critical infrastructure. Its enterprise software solutions leverage operational, financial and asset data to empower its clients to make investment decisions that deliver the highest business value. Copperleaf and NGET worked together to combine the power of the Copperleaf optimisation platform, Copperleaf ’s consulting and software development expertise, with the engineering knowledge and prototypes that have been nurtured within National Grid over many years. “This is the most complex optimisation problem Copperleaf has ever solved” (Stan Coleman, CTO Copperleaf ).

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The following approach was developed to divide the optimisation into a sequence of steps. Specifically: Step 1: Asset Level Value Model. This value model considers: • monetised risk which is calculated from asset history, asset age, network risk, and failure mode effect analysis data.

• up to eight different interventions per asset: basic/intermediate or major maintenance, minor or major refurbishments and replacement of the entire asset or individual components.

• lifecycle costs and value (in terms of risk mitigation) of various intervention schedules e.g. basic, refurbishment, replacement on a five-year cycle.

Step 2: Circuit Optimisation. Each circuit was optimised multiple times with different constraints. Because each circuit is relatively small it is possible to express this as a MixedInteger Linear Programme (MILP) and identify optimal solutions for each circuit quickly. The output of the stage one optimisation was multiple bundled solutions for each circuit.

a reasonable amount of time. Using the multi-step technique, NGET and Copperleaf were able to run network wide optimisations involving more than 60,000 assets, considering over 5,000 interventions per year, with 50 teams each using up to five different resource type and 100 overlapping boundaries for circuit constraint management.

Using the multi-step approach described, we were able to find what industry experts deemed to be a valid solution to a problem, where previously it was not possible to find a solution.

A patent application for the multi-step technique is in progress.

EMBEDDING THE SOLUTION

The challenge with complex analytical solutions such as this is to truly embed new capability into business processes. Far too often a valuable solution is ‘owned’ by a small number of individuals in a silo who are the

Step 3: Outage Duration Calculation.

The outage duration for each of the solutions was computed.

Step 4: Network Wide Optimisation. In this optimisation, each of the bundled solutions computed in step 2 and the network-wide outage and resource constraints are considered. Because the bundling gains were already computed in Step 2, and because the non-linear duration calculation was implemented as part of Step 3, the problem can be expressed as a MILP that is possible to solve even for a large network in

© National Grid

There was no current industry practice for solving this problem as existing solutions can only handle a smaller number of assets and cannot support the non-linear outage duration calculations. The technical uncertainty was therefore whether or not an algorithm could be found that could compute the optimal intervention schedule.

The increased agility and speed to carry out what-if analyses has been particularly important as NGET has had to deconstruct the plan and rebuild under different scenarios through the course of the COVID-19 pandemic

BENEFITS

Having gone live in September 2019, the new capability is delivering significant benefits. These include: • Centralised Platform: one simple platform for monetised risk, asset intervention planning and circuit optimisation.

Instead of having to look in five different tools or spreadsheets, NGET now has everything available in a single screen in the Copperleaf Solution.

• Planning Efficiency of 50%: Reducing the time to build the plan for fiscal

across the electricity transmission network where there is a requirement to recruit and build resource capacity.

year 2022 by 50% (from 12 weeks to 6 weeks). The increased agility and speed to carry out what-if analyses has been particularly important as NGET has had to deconstruct the plan and rebuild under different scenarios through the course of the COVID-19 pandemic.

• Reduction in Outage Costs of £6m per year: The Copperleaf Solution has reduced outage requirements across the network – for example, the number of circuits requiring an outage three times in a five-year period has reduced from 18% to just 8%.

Specifically, compared to manual bundling, the Copperleaf optimisation for the FY22 plan has increased the “Bundling Ratio” (number of interventions per outage) by 66%.

This aims to save up to 1,000 network outages and £6m in corresponding outage setup costs.

• Higher Value Plans: enhanced asset lifecycle modelling leading to improved risk management and ensuring NGET selects the right interventions at the right time and the right cost for consumers.

• Strategic Workforce Planning: The output from the Copperleaf optimiser has also provided greater visibility into long-term resource requirements and pinch-points

David Wright, Director of Electricity Transmission, National Grid: “The Circuit Optimiser platform is underpinned by a cutting-edge and complex algorithm that was co-developed with Copperleaf. This is helping National Grid Electricity Transmission further improve our ability to anticipate and manage asset risks, ensuring we continue to make the right investment decisions across our network.”

This is helping National Grid Electricity Transmission further improve our ability to anticipate and manage asset risks Stefan Sadnicki is Copperleaf ’s Managing Director for Europe, Middle East and Africa (EMEA). Reporting directly to the CEO, Stefan joined Copperleaf as employee #1 in Europe in 2014 and has since led the development of Copperleaf ’s business and team of 50 distributed across EMEA. Prior to Copperleaf, Stefan was a Management Consultant with Capgemini Consulting where he worked in the cross-sector Operational Research consulting team, which itself had its origins in the OR groups at British Gas and British Coal. Stefan graduated from Queen’s College, Oxford University with a first-class degree in Mathematics and an MSc. with distinction in Computer Science. He also has a Post Graduate Certificate in Operational Research from Strathclyde University and an Asset Management Diploma with distinction from the Institute of Asset Management (IAM).

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© National Grid

only ones able to understand the data input process, the algorithms and then interpret the results. What makes this project stand out is how NGET has embedded the capability to be a central part of their business as usual asset investment planning process.

Having developed prototypes using Python, NGET made a conscious decision to implement the solution using a commercial off the shelf (COTS) solution. Data input has been automated by integrating asset, maintenance and investment data from existing enterprise systems. There are eight programmatic integrations which run either nightly (e.g. to import updated asset data), monthly (e.g. to bring in existing investments, such as a commitment to connect a customer, that are fixed in the schedule) or when required (e.g. to update FMEA risk model inputs).

THIS YEAR, NEXT YEAR, SOMETIME, NEVER? Geoff Royston

1720, is quoted as saying that “I can calculate the motions of the heavenly bodies, but not the madness of people”) feature important non-linearities, e.g. from feedback effects, where initially imperceptible fluctuations can quickly amplify over time and introduce ever-growing errors into forecasts; the so-called butterfly effect. However precise our current knowledge of such complex dynamic systems, these compounding errors will eventually swamp our ability to predict their future.

THE TRUMPET OF UNCERTAINTY

The kwik brown foks jumpd ovr the lazee dog. That could be correct spelling - if the forecasts made at the dawn of the twentieth century by the engineer John Elfreth Watkins had been right. In 1900 he made a number of predictions, including that by the 2000s, English language will be condensed, with “no C, X, or Q in our everyday alphabet.”

We will come back later to foxes and dogs (or rather, hedgehogs), and indeed to John Watkins’ forecasts.

Meantime, let’s look at problems of prediction.

This problem can be viewed as the trumpet of uncertainty, see Figure 1, where uncertainty is visualised as proportional to the product of time and complexity. The trumpet is narrow for the highly predictable (e.g. clocks, planetary motions, tomorrow’s weather), wide for the highly unpredictable (e.g. the weather in a month’s time, stock market fluctuations, revolutions) and in between for things in the middle (e.g.

UK birth rate ten years from now, the rise and fall of an epidemic, the extent of climate change).

There is no shortage of forecasts that have been spectacularly wrong. Take for example, the world of information technology. In 1943 the president of IBM predicted a world market "for maybe five computers"; in 1977 the head of the DEC (Digital Equipment Corporation) said “there is no reason why anyone would want a computer in their home”; and in 2007 the CEO of Microsoft said “there’s no chance that the iPhone is going to get any significant market share”.

So, is forecasting just a load of crystal balls? Before looking into some empirical evidence, let’s consider first why prediction is difficult. Apart from the obvious factor of ignorance – lack of knowledge or understanding of (or disregard for) crucial elements of a situation - two key factors are time and complexity. Our ability to see into the future is generally less the further ahead we are trying to look and the greater the complexity of the system we are considering. Some real-world systems obey simple laws and are highly predictable – for example Edmund Halley in 1705 accurately foretold the return 53 years later of the comet that accordingly now bears his name. Many others however (not least those where human behaviour is involved – Newton, who lost a fortune in the South Sea Bubble of

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© Geoff Royston

CRYSTAL BALLS?

FIGURE 1 THE TRUMPET OF UNCERTAINTY

Can we learn to blow the trumpet of uncertainty?

That could be the leitmotif for a widely praised book; Superforecasting: The Art and Science of Prediction, written by Philip Tetlock with co-author Dan Gardner.

FOXES AND HEDGEHOGS

Tetlock wanted to test the accuracy of experts’ predictions.

So he asked several hundred of them to make forecasts in areas like elections, the economy and so on and then looked (over a period of 20 years!) at the results. The experts on average scored about as well as would a dart throwing chimpanzee!

So, forecasting in such areas is for the deluded, case closed? Far from it. First, many of the predictions requested were near, or even in, the zone of the unpredictable, at least for far ahead. Second, note the word “average”; experts’ forecasting skills were not all the same. Tetlock found two groups in particular, which he termed “hedgehogs” and “foxes”, (after the words of the Greek poet Archilocus, “the fox knows many things but the hedgehog knows one big thing”). Tetlock’s hedgehogs were the one-track thinkers who tried to fit facts to their preconceptions; his foxes were those who used a range of analytical approaches, considered matters from several different perspectives and used a variety of sources of information. Hedgehogs were confident in their forecasts - but their performance was (slightly) worse than random guessing! Foxes were less sure of themselves but they beat the hedgehogs, scoring (a bit) above chance levels.

Tetlock then followed up his original experiment with an even larger one involving 20,000 interested members of the public making forecasts. He found that the predictions of his volunteers were better – and with practice, became increasingly so - than those of control groups of experts. And some forecasters did much, much, better than average (and this was not a fluke – their performance was sustained); these were the superforecasters. They could forecast 300 days ahead as successfully as the other volunteers managed in looking ahead just 100 days.

SUPERTHINKING

Tetlock found that his superforecasters used ways of thinking that built on the “fox-like” attributes mentioned above. His book lists ten of these; here are my summaries of three of them.

Triage. Decide if the question is in the highly predictable, highly unpredictable or in-between region, and devote most of your forecasting efforts to ones in that middle “Goldilocks” zone.

“Fermi-ise”. (After the “back-of-the envelope” estimation approach of the nuclear physicist Enrico Fermi.)

Break down difficult forecasting questions into their determining components, think about these separately and then put that all back together to produce a combined result.

Think Bayesian. (Following the statistical philosophy of the Rev Thomas Bayes.) The basic approach is the same,

whether or not Bayes’ equations are deployed; start with a sensible baseline forecast (for example “no change” is generally a good starting point for predicting tomorrow’s weather). Adjust this – but not too much - for each piece of new evidence.

These all seem sound advice to me – indeed I have discussed the last two, in different contexts, in previous articles in Impact. I would, however, add a couple of caveats to the recommendation on triage. First, many “highly predictable” forecasting questions, that yield to maths and modelling, need and are worth spending time on – else insurance analysts and actuaries would be out of a job. Second, while “highly unpredictable” forecasting questions take us into areas where trying to guess what will happen may be only for the brave or foolhardy, thinking about what could happen is always valuable – and is the realm where scenario thinking comes into its own - and can take us beyond forecasting: as Abraham Lincoln said, the best way of predicting the future is to create it!

SWALLOWS AND SWANS

Tetlock’s advice prompts me to mention another renowned name in the forecasting world, Nate Silver, and his acclaimed (and recently updated) book on the art and science of prediction, The Signal and the Noise. In a world of ever-increasing amounts of data, much of which is irrelevant (or wrong), distinguishing the key information (the signal) from the rest (the noise) gets ever harder. (A positive diagnostic test has low predictive value if the true results are swamped in a sea of false positives.) A central theme of Silver’s book is the need to take a Bayesian perspective in forecasting, considering each piece of additional information within the wider context of the past evidence and understanding you already have about a situation.

This reduces the risk of taking noise to be signal and overweighting a single piece of new evidence - one swallow does not make a summer.

However, an eye also needs to be kept out for abnormally strong evidence, especially in situations where past experience may not provide a good baseline for the future – the first sight of just one black swan did prove that not all swans are white. “The Signal and the Noise” notes that some of the worst forecasting errors, such as failures to anticipate the Pearl Harbour attack in 1941 and the New York Trade Centre bombings in 2001, came from not even considering some of the possibilities (Donald Rumsfeld’s “unknown unknowns”,

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I promised to return to John Watkins, to whom I rather dismissively referred earlier. To be fair, around half of the predictions he made back in 1900 for the 20th century were remarkably near the mark – for instance he foresaw live TV news, real-time whole-body medical imaging - and home delivery of ready-to-eat meals. And even his quirky alphabetic prophecy looks less off-target if you look at the condensed wording; 2NITE, GR8, WYSIWYG, …. often used in mobile phone text messaging. Maybe not quite as impressive a record as that of Edmund Halley, but then he was operating at the near-opposite end of the predictability spectrum. Watkins and Halley were clearly proficient players of the trumpet of uncertainty, but as Tetlock’s and Silver’s books indicate, all of us can improve our performance.

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Dr Geoff Royston is a former president of the OR Society and a former chair of the UK Government Operational Research Service. He was head of strategic analysis and operational research in the Department of Health for England, where for almost two decades he was the professional lead for a large group of health analysts.

© Crown Publishing Group/Penguin Random House

NOT SO KWRKEE?

© Reproduced by permission of The Random House Group Ltd/Penguin Books Ltd.

or maybe “known unknowns” – possibilities we do not want to consider.)

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