AHybridPlithogenicDecision-MakingApproach withQualityFunctionDeploymentforSelecting SupplyChainSustainabilityMetrics
MohamedAbdel-Basset 1,*,RehabMohamed 1,AbdEl-NasserH.Zaied 1 andFlorentinSmarandache 2
1 DepartmentofOperationsResearch,FacultyofComputersandInformatics,ZagazigUniversity, Sharqiyah44159,Egypt
2 Math&ScienceDepartment,UniversityofNewMexico,Gallup,NM87301,USA
* Correspondence:analyst_mohamed@zu.edu.eg
Received:20June2019;Accepted:4July2019;Published:11July2019
Abstract: Supplychainsustainabilityhasbecomeoneofthemostattractivedecisionmanagement topics.Therearemanyarticlesthathavefocusedonthisfieldpresentingmanydifferentpointsof view.Thisresearchiscentredontheevaluationofsupplychainsustainabilitybasedontwocritical dimensions.Thefirstistheimportanceofevaluationmetricsbasedoneconomic,environmental andsocialaspects,andthesecondisthedegreeofdifficultyofinformationgathering.Thispaper aimstoincreasetheaccuracyoftheevaluation.Theproposedmethodisacombinationofquality functiondeployment(QFD)withplithogenicaggregationoperations.Theaggregationoperation isappliedtoaggregate:Firstly,thedecisionmaker’sopinionsofrequirementsthatareneededto evaluatethesupplychainsustainability;secondly,theevaluationmetricsbasedontherequirements; andlastly,theevaluationofinformationgatheringdifficulty.Tovalidatetheproposedmodel,this studypresentedarealworldcasestudyofThailand’ssugarindustry.Theresultsshowedthemost preferredandthelowestpreferredmetricsinordertoevaluatethesustainabilityofthesupply chainstrategy.
Keywords: supplychainsustainabilitymetrics;plithogeny;aggregationoperations;neutrosophicset; qualityfunctiondeployment
1.Introduction
Supplychainsustainabilityhasbeenoneofthemostattractiveanddynamicresearchtopicsinthe domainofsupplychainmanagementforalongtime.Theinfluenceofmanufacturingactivitiesto globalwarmingandtheconsumptionofnaturalresourcesassistedtheresearchersinconsideringthe importanceofthesupplychainoperation’ssustainability[1].Asaresultofincreasingcompetition, globalization,technologicalgrowthandhugecustomerexpectations,thesustainablesupplychain isasignificantgoaltoeachsupplychainineveryfield.Thesupplychainsustainabilitycanbe describedasthecapabilityofoperatingthebusinesswiththelongtermgoalofpreservingeconomic, environmentandsocietalwelfare[2].Amoregeneraldefinitionofsustainablesupplychaincouldbethe managementofsupplychainactivitiesinordertoimprovetheprofitabilitybytakingintoconsideration theenvironmentalimpactsandsocialaspects.Therefore,supplychainsustainabilityguarantees successandachievementsofthewholesupplychainmanagementinthelongterm.Underthe uncertaintycomponent,supplychainsustainabilitybecameamoreimportantgoalforcompanies.This explainswhymeasuringsupplychainsustainabilitymeanstoidentifypossiblestrategicdecisions undervarioussituations[3].
Symmetry 2019, 11,903;doi:10.3390/sym11070903
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Theevaluationofsupplychainsustainabilityisaninterestingtopicbasedonmetricsin economic,environmentalandsocialscopes.Measuringsustainabilityofthesupplychainguides firmsinthedirectionofriskeliminationandstandards/guidelinesfollowing[4].Moreover, theadvantagesofevaluatingsupplychain(SC)sustainabilityarereducingcosts,increasingcompetence, supportingcompetitiveadvantagesandimprovingoperationalperformance[5].Thechallengesof measuringsupplychainsustainabilityare[6]:Themanagerialandorganizationalabsenceofthe inter-organizationalmetrics;thevarietyoftheorganization’sgoalsandobjectivesproducingdifferent measures;andthedifficultyinnon-traditionaldatagatheringthatreducetheSCperformance.
Thereareseveralstudiesinsupplychainsustainabilityassessmentincludingsupplychain sustainabilityriskandassessment[7],literaturereviews[8],multi-objectivemathematicalmodels forsustainablesupplychainmanagement[9]anddecisionmakingmodelsforasustainablesupply chain[10].Evaluatingsupplychainsustainabilityisamulti-criteriadecisionmaking(MCDM)problem, thereforetheevaluationmetricsmaybethecriteria,andthealternativesmaybeselectedbasedon thesesetsofmetrics.TherearesomelimitationsofSCsustainabilitystudies,suchasthefactthatthe researchersdonotconsiderthedifficultyofcollectingtheinformationforthemetricsthatwillmeasure thesustainability.Inaddition,onlyafewstudiesusethelinguisticvariablestoevaluatethemetrics, leadingtolessconsiderationontheuncertaintyorlackofinformation[11].Also,thereisthematterof thedecisionmaker’sprioritiesandcontradictiondegreebetweenmetricswhichleadstolessaccuracy ofresults.Inthecomicalindustry,Rajeev(2019)proposedaframeworktodescribetheevolutionof asustainablesupplychain[12].
Inthisresearch,mostoftheselimitationswereprocessedbytheproposedMCDMmodelthat assistsinmetricsselectionandtheweightingofsustainablesupplychain.Theproposedmodelis basedonacombinationofplithogenicaggregationoperationswithqualityfunctiondeployment(QFD). ThedetailsofthemodelhavebeenexplainedinSection 3.
QFDisoneofthemostpopulartechniquestoimprovequalityinordertomeetcustomer expectations.Thistoolcombinesallcustomerneedsineveryaspectoftheproduct,transformingthem intotechnicalrequirementssotheycanmeettheirexpectations[13].QFDrecordsgreatresultsin manyfields,suchasratingengineeringcharacteristics[14],thedesignofbuildingstructures[15], servicelevelmeasurements[16],industrydevelopment[17],productdevelopment[18],orsupplier selectionproblems[19].
Plithogenyreferstothecreation,developmentandprogressionofnewentitiesfromcomposition ofcontradictoryornon-contradictorymultipleoldentities[20].ItwasintroducedbySmarandache in2017asageneralizationofneutrosophy.Aplithogenicset(asageneralizationofcrisp,fuzzy, intuitionisticfuzzy,andneutrosophicsets)isasetwhoseelementsarecharacterisedbytheattribute values.Eachattributevaluehasitscontradictiondegreevalues c(vj,vD) between vj andthedominant (mostimportant)attributevalue vD.Thecontradictiondegreebetweenattributesassiststhemodel togainmoreaccurateresults.Theplithogenicset,logic,probabilityandstatisticsthatwerealso introducedbySmarandachein2017,areobtainedfromplithogeny,andtheyaregeneralizationsof neutrosophicsets,logic,probabilityandstatisticsrespectively.
Therestofthispapersisorganizedasfollows:InSection 2,thereisaliteraturereviewofsustainable supplychain,qualityfunctiondeployment,clarificationofplithogenicsets,andarecapitulationof neutrosophicsets.Section 3 presentstheproposedmodeltoevaluatethesustainablesupplychain. InSection 4,arealworldcaseisstudiedinordertoevaluatetheproposedmodel.Section 5 discusses theresultsofthiscase.Finally,theconclusionandsuggestionsforfutureworksendSection 6.
2.LiteratureReview
2.1.SupplyChainSustainability
Inthesupplychainmanagementfield,therearemanyconsiderationsthatneedtobetaken intoaccounttominimizethenegativeinfluenceofbusinessproductiontoenvironmentandsocial
e
ff
ects.Theseconsiderationspushedforthestrategicdevelopmentsplansforsustainability[11]. Threedimensionsofsustainabilityareconsideredinthesupplychain,derivedfromcustomerand stakeholderdesires,whichareeconomic,environmentalandsocialaspectstomanagerawmaterials, informationandfinanceflows[21].Otherdefinitionsofsupplychainsustainabilityistheintegrationof anorganization’seconomic,socialandenvironmentaldimensionsbycoordinatingthebusinessprocess inordertoimprovetheorganization’sperformanceinthelongterm[22].Amorefocuseddefinition couldbesupplychainmanagementstrategiesandactivitiesconcerningsocialandenvironmental aspects,correlatedtotheproduction,distribution,designandsupplyofproductsandservices[23]. Theevaluationofsupplychainsustainabilitymetricsareattributesandrequirementsusedtomeasure thesupplychainperformanceconsideringeconomic,socialandenvironmentalfeatures[24].Table 1 summarizessomeofthestudiesonsupplychainsustainabilitymetricsandframeworks.
Table1. Studiesaboutsupplychainsustainabilitymetrics.
AuthorsScopeMethodologyMetrics
AkshayJadhav,StuartOrr, MohsinMalik(2018)[25]
ElkafiHassini,ChiragSurti, CorySearcy(2012)[4]
Yazdani,Morteza,Cengiz Kahraman,PascaleZarate, andSeziCevikOnar(2019)[26]
Supplychainorientation (SCO)
Developingsupplychain sustainabilitymetrics
Literaturereview analysis(SEManalysis)
Literaturereview
Co2emissionmanagement, communityengagement,supplier codesofconduct,wasteelimination, energyusageefficiency,waterusage efficiency,andrecycledmaterials practices,amongothers.
Percentofsuppliers,Percentof contracts,Percentofpurchase orders,Levelofstake-holdertrust bycategory
Rankingofsupplychain sustainabilityindicators
Qorri,Ardian,ZlatanMujki´c,and AndrzejKraslawski(2018)[27]
Searcy,Cory,ShaneM.Dixon,and W.PatrickNeumann(2016)[28]
Chen,Rong-Hui,YuanhsuLin, andMing-LangTseng[29]
Haghighi,S.Motevali,S.A. Torabi,andR.Ghasemi[30]
Multi-attributedecision making(QFDandGRA)
Measuringsupplychain sustainability performance Literaturereview
Analysisofperformance indicatorsinsupply chainsustainability
Sustainabledevelopment indicatorsinthe structureminerals industryinChina
EvaluationofSustainable SupplyChainNetworks
2.2.QualityFunctionDeployment(QFD)
Literaturereviewand reportanalysis
Combinesfuzzyset theory,theDelphi method,discrete multi-criteriamethod
Dataenvelopment analysistechnique
Quality,managingenvironmental systems,supplychainelasticity, businesssocialliability, transportationservicesituation, andfinancialconstancy.
Numberofcontributors,products, geographicalencompassing, strategicgoals,methods,tools, amongothers
Employeesnumber,profits, supplierestimation,trainingcost, amongothers
Solidwaste,Eco-efficiency, Healthandsafety,Energyuse, Investments,Landuseand rehabilitation,amongothers
Timedelivery,Supplierrejection rate,AmountofPollution, Customers’satisfaction, Servicequality,amongothers
• Area(1):Thecustomers’requirements(whatregion)thatconsistsoftwoindicators:Thecustomers’ requirementsandtheimportanceofeachofthem αi
• Area(2):Thequalitycharacteristicsordesignspecifications(howregion),composedoftwoparts: Thedesignspecificationsandthewayofdevelopment.
• Area(3):Therelationshipbetweencustomerrequirementsanddesignspecifications(whatversus howregion)byscore Cij = {0,1,3, ,9}
:
customers’ requirements and the importance of each of them �� .
• Area (2): The quality characteristics or design specifications (how region), composed of two parts: The design specifications and the way of development.
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• Area (3): The relationship between customer requirements and design specifications (what versus how region) by score �� = 0,1,3,…,9 .
• Area (4): This area is a combination of the value of the design specification, the acceptance level of it, and the score
• Area(4):Thisareaisacombinationofthevalueofthedesignspecification,theacceptancelevelof it,andthescore
S j = αi ∗ Cij (1)
�� = (�� ∗ �� ) (1)
• Area(5):Thecomparisonoftheproductandcompetitors,andhowmuchitsatisfiescustomerneeds.
• Area (5): The comparison of the product and competitors, and how much it satisfies customer needs.
• Area(6):Thecomparisonbetweeneachdesignspecification,andhowmuchtheirimprovement mayaffecteachother.
• Area (6): The comparison between each design specification, and how much their improvement may affect each other.
Figure 1. QFD structure Figure1. QFDstructure.
ThereareseveralstudiesthatcombineQFDwithothertechniquestoevaluatesupplychain sustainability,suchas:AhybridQFD–ANPapproachtodesignasustainablemaritimesupplychain[33]; integrationofQFDandgreyrelationalanalysis(GRA)inordertosolvecompounddecisionmaking complications[26];QFDandMCDMtechniquesinsupplierselectionproblems[34]. Dursunetal.(2018) consideredthecompetitionfactorintheprocessofnewproductdevelopmentusingQFD[35]. Acombinationofbest-worstmethod(BWM)andQFDwasproposedinordertodeterminetherelation betweencustomerrequirementsandengineeringcharacteristicsinMeietal.(2018)[36].
2.3.PlithogenicSetCharacteristics
Plithogenyistheformation,construction,development,germination,andevolutionofnew entitiesfromcombinationsofcontradictory(dissimilar)ornon-contradictorymultipleoldentities[37]. Aplithogenicset (P, A, V, d, c) isasetthatincludesnumerouselementsdescribedbyanumberof attributesA = {α1, α2, , αm}, m ≥ 1,whichhasavaluesV = {v1,v2, ,vn},for n ≥ 1.Therearetwo mainfeaturesofeachattribute’svalue,V.Thefirstistheappurtenancedegreefunction d(x,v) ofthe element x,withrespecttosomegivencriteria[38].Thecontradiction(dissimilarity)degreefunction c(v,D) isthesecondone,whichisrealizedbetweeneachattributevalueandthemostimportant (dominant)one.Thecontradictiondegreefunctionismainlythekeyelementoftheplithogenic aggregationoperations(intersection,union,complement,inclusion,andequality)thatincreasethe accuracyofaggregation.
LetAbeanon-emptysetofuni-dimensionalattributesA = {α1, α2, , αm}, m ≥ 1,andlet α ∈ A beanattributewithitsvaluespectrumtheset S,whereScanbedefinedasafinitediscreteset,
S = {s1, s2, ... , sl}, 1 ≤ l <∞,orinfinitelycountableset S = {s1, s2, ... , s∞},orinfinitelyuncountable (continuum)set S = ]a,b[, a < b,where ] ... [ isanyopen,semi-open,orclosedintervalfromthesetof realnumbersorfromothergeneralsets[39].
Let V beanon-emptysubsetof S,where V istherangeofallattributesof α’svaluesdefinedbythe expertsbasedontheapplication, V = {v1, v2, , vn} for n ≥ 1.Intheset V,thereisadominantattribute valuewhichisdeterminedbytheexpertsbasedonpreferencesandthenatureoftheapplication.
Eachattributevaluein V hasitsappurtenancedegree d(x,v) withrespecttosomecriteria.Thedegree ofappurtenancemaybeafuzzy,orintuitionisticfuzzy,orneutrosophicdegreeofappurtenancetothe plithogenicset.Therefore,theappurtenancedegree d(x,v) ofattributevalue v is:
∀x ∈ P, d: P × V → P([0,1]z),(2)
Therefore, d(x, v) isasubsetof[0,1]z,andP([0,1]z)isthepowersetof[0,1]z,wherez = 1,2,3, forfuzzy,intuitionisticfuzzy,andneutrosophicdegreesofappurtenancerespectively[19].
Let c: V × V → [0,1]betheattributevaluecontradictiondegreefunction c(v1, v2),representingthe dissimilaritybetweentwoattributevalues v1 and v2,andsatisfyingthefollowingaxioms:
c(v1, v1) = 0,contradictiondegreebetweentheattributevaluesanditselfiszero. c(v1, v2) = c(v2, v1),contradictiondegreefunctioncanbefuzzyCF,intuitionisticattributevalue contradictionfunction(CIF: V × V → [0,1]2),oraneutrosophicattributevaluecontradictionfunction (CN: V × V → [0,1]3).
2.4.NeutrosophicSet
Neutrosophyisanewbranchofphilosophy(generalizationofdialecticsandYinYangChinese philosophy),introducedbyFlorentinSmarandachein1980,whichstudiestheorigin,nature,andscope ofneutralities,aswellastheirinteractionswithdifferentideationalspectra.Neutrosophyisthe foundationofneutrosophiclogic,neutrosophicprobability,neutrosophicsets,andneutrosophic statistics.Neutrosophicsetdefinitionsareclearlystatedinthefollowing:
Definition1. [40]LetXbeauniversalsetofobjects,consistingofnon-specificelementsdefinedasx. AneutrosophicsetN ⊂ XreflectsasetsuchthateachelementxfromNischaracterizedbyTN(x)–the truth-membershipfunction,IN(x)–theindeterminacy-membershipfunction,andFN(x)–thefalsity-membership function.TN(x),IN(x)andFN(x)aresubsetsof[0 ,1+],sothethreeneutrosophiccomponentsareTN(x) ∈ [0 , 1+],IN(x) ∈ [0 ,1+]andFN(x) ∈ [0 ,1+].IN(x)isdepictsuncertainty,indeterminate,unidentified,orerror values.Thesumofthethreecomponentsis0 ≤ TN(x) + IN(x) + FN(x) ≤ 3+ .
Definition2. [41]LetXbeaspaceofpointsandx ∈ X.AneutrosophicsetNinXisrecognizedby atruth-membershipfunctionTN(x),anindeterminacy-membershipfunctionIN(x)andafalsity-membership functionFN(x),whereTN(x),IN(x)andFN(x)aresubsetsof]-0,1+[.TN(x):X → ]-0,1+[,IN(x):X → ]-0,1+[ andFN(x):X → ]-0,1+[.Thereisnorestrictiononthesummationofmembershipfunctions.Therefore,0−≤ sup TN(x) + supIN(x) + supFN(x) ≤ 3+
Definition3. [42]Let a = (a1, a2, a3); α, θ, β beasinglevaluedtriangularneutrosophicset,withtruth membershipTa(x),indeterminatemembershipIa(x),andfalsitymembershipfunctionFa(x)asfollows:
= a2 ootherwise (3)
(5) where αa, θa, βa ∈ [0,1].Theyrepresentthehighesttruthmembershipdegree,thelowestindeterminacy membershipdegree,andthelowestfalsitymembershipdegree,respectively.
3.ProposedModel
Inthispaper,theauthorsproposedamodeltoevaluatethesupplychainsustainabilitymetrics basedonacombinationofqualityfunctiondeploymentandplithogenicaggregationoperations. ThismodelcombinesthebenefitsoftheQFDmethodtolinkcustomerneedswithdesignrequirements andplithogenicaggregationoperatorfeatures.Theusefulnessofthismodelderivesfromthe plithogenicaggregationoperation,becausethistechniqueensuresmoreaccurateresultsandtakes intoconsiderationthedegreeofuncertainty,whichisdefectiveinotherstudiesofthesameproblem. Thestepsoftheproposedmodelhavebeenexplainedindetailinthissectionanditisshownin Figure 2
❖ Step1: Firstofall,decisionmakers(DM)identifyaseriesofrequirementstoappraisethesupply chainsustainability.Themostpopularrequirementsofsupplychainsustainabilityevaluation aresummarizedinTable 2 ortheDMcanidentifyotherrequirementsbasedontheirstrategy. Theserequirementsmustreflecteconomic,socialandenvironmentalfeatureswhichiscalled triplebottomline(TPL).
Thedecisionmakersmeasuretheimportanceofeachrequirementbasedonthesupply chainstrategyusinglinguisticterms.
ThelinguisticscaleisdefinedtodescribetheassessmentofeachrequirementbytheDM. Inthismodel,thescaleissuggestedasatriangularneutrosophicscale,asshowninTable 3
❖ Step2: Usingplithogenicaggregationoperations,thedecisionmaker’sopinionsareaggregated basedonthecontradictiondegreeofeachrequirement.Thisstepincreasestheaccuracyofresults.
Definecontradictiondegree c ofeachrequirementwithrespecttothedominant. Plithogenicneutrosophicsetintersectionisdefinedasfollowing:
❖ Step3: Inordertofindthebestrequirementconsideringthesetofcriteria,thedistanceofeach requirementisfoundfromthebestandworstsolutions.
Thebest(positive)idealsolution S+ andworst(negative)idealsolution S requiredefinition.
Forexample,inpricerequirements,thelowestvalueisdesired(bestidealsolution);onthe otherside,themaximumvalueistheworstidealsolution.Intheoppositeofthat,inprofit requirements,themaximumvalueispositiveandthelowestvalueisnegative.
TheformulaofEuclideandistanceisusedtofindthedistanceofeachrequirementtothe idealpositiveandidealnegativesolutions,asshowninEquations(8)and(9)[50].
Thesuperioralternativehasthesmallestdistancefromthepositiveidealsolution S+ and theworstalternativehasalargerdistancefromthenegativeidealsolution S .
❖ Step4: Theperformancescoreofeachrequirementisfoundinordertoweighteachofthem basedonEquation(10). Pi = Si S+ i Si (10)
Theperformancescoreisnormalizedtofindtheweightofeachrequirementthatsatisfies twoconstraintswhichare0 ≤ wi ≤ 1and wi = 1.
❖ Step5: Thedecisionmakersdefineacombinationofmetricsbyconsideringtherequirements selectedpreviouslyinstep1andtheTPL.Someoftheeconomicmetricsarecostreduction, transactioncosts,environmentalcosts,servicelevel,orsales.Theenvironmentalmetricsare environmentalpolicies,recyclingofwaste,airpollutionemission,solidwaste,waterconsumption, andsoon.Finally,thesocialdiminutionconsistsofworkingconditions,employeesatisfaction, governmentrelationships,employeetraining,andreputation,amongothers.
TheDMsdefinetherelationbetweeneachmetricandexplaineachrequirementusing linguistictermsasinTable 3
❖ Step6: Steps2–4arerepeatedontheevaluationmetrics.AsinStep2,theplithogenicaggregation operationisusedtocombinealldecisionmakers’judgmentsaboutdefinedmetrics.Then, Equations(8)and(9)areusedtoestablishthedistanceofeverymetricfromthebestandworst solutions.Theimportanceofeachmetricisdeterminedusingtheperformancescoreasin Equation(10).
❖ Step7: AsproposedinOsiro,Lauroetal.2018[11],thelimitationsofotherstudiesthatdonot considerthehardnessofdatagatheringofeachmetricneedtobeaddressed.Inthisstep,the difficultyinregardstothreedimensionsareevaluatedinrelationtoinformationaccessibility, humanresourcesandtimeneededforassessment,andotherrequiredresources[51].
Thedifficultyofassessmentmetricsdatacollectingbasedonthreedimensionsexplained bylinguisticvariablesareevaluated.
Theassessmentbasedonthecontradictiondegreetoobtainaccuracyofresultsare aggregated,andthenitscrispvalueisfound. Theirperformancescoreofdatacollectingdifficultybasedonthedistanceofbestand worstsolutionsarefound.
❖
Step8: Inthisfinalstep,thegoalistocategorizethesetofsupplychainsustainabilitymetrics.
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TheperformancedegreefoundinStep4(theimportanceofeachmetric)andStep7 (thedifficultyofdatagathering)usingEquation(11)arenormalizedasproposedin (Osiro,Lauroetal.2018)[11].
vn = 1 1 + e v v σv (11)
Step 1: First of all, decision makers (DM) identify a series of requirements to appraise the supply chain sustainability. The most popular requirements of supply chain sustainability evaluation are summarized in Table 2 or the DM can identify other requirements based on their strategy. These requirements must reflect economic, social and environmental features which is called triple bottom line (TPL).
where vn isthenormalizedvalue, v v isthedifferencebetweenthevalueandthemean, and σv isthestandarddeviation.
Thisistheresultifsupplychainsustainabilityevaluationmetricsarecategorizedaccording toFigure 3 basedontheimportanceofeachmetricanditsdifficultyofdatagathering.
The decision makers measure the importance of each requirement based on the supply chain strategy using linguistic terms.
Figure 2. Steps of the proposed model.
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Figure2. Stepsoftheproposedmodel.
The linguistic scale is defined to describe the assessment of each requirement by the DM. In this model, the scale is suggested as a triangular neutrosophic scale, as shown in Table 3.
Step 2: Using plithogenic aggregation operations, the decision maker’s opinions are aggregated based on the contradiction degree of each requirement. This step increases the accuracy of results.
Define contradiction degree of each requirement with respect to the dominant. Plithogenic neutrosophic set intersection is defined as following: = ,1≤��≤��. (6) where ⋀ and
Figure3. Twodimentionsmodelcategorization.
Figure 3. Two dimentions model categorization.
4. Real World Case Study
In this paper, the proposed model has been illustrated in an application on Thailand’s sugar industry in order to measure the overall sustainability of this supply chain (Figure 4). The sugar industry in Thailand is considered one of the most important economic pillars. Thailand is the fourth largest sugar producer and second largest exporter in the world. In this application, four decision
Table2. Popularrequirementsofsupplychainsustainabilityevaluations.
RequirementAuthor
1Cost/profit
2Productquality
Govindan,Kannan,RoohollahKhodaverdi, andAhmadJafarian[43]
Osiro,Lauro,FranciscoR.Lima-Junior, andLuizCesarR.Carpinetti[44]
3EnvironmentalinfluencesHuang,SamuelH.,andHarshalKeskar[45]
4StabilityandconstancyKannan,Devika,etal.[46]
5InformationTechnology
Katsikeas,ConstantineS.,NicholasG.Paparoidamis, andEvaKatsikea[47]
6SocialaspectsMani,V.,RajatAgrawal,andVinaySharma[48]
7DeliveryaccuracyChang,Betty,Chih-WeiChang,andChih-HungWu[49]
Table3. Linguisticscale.
LinguisticVariableTriangularNeutrosophicScale
Nothing(N)((0.10,0.30,0.35),0.1,0.2,0.15)
VeryLow(VL)((0.15,0.25,0.10),0.6,0.2,0.3)
Low(L)((0.40,0.35,0.50),0.6,0.1,0.2)
Medium(M)(0.65,0.60,0.70),0.8,0.1,0.1)
High(H)((0.70,0.65,0.80),0.9,0.2,0.1)
Veryhigh(VH)((0.90,0.85,0.90),0.7,0.2,0.2)
Absolute(A)((0.95,0.90,0.95),0.9,0.10,0.10)
4.RealWorldCaseStudy
Inthispaper,theproposedmodelhasbeenillustratedinanapplicationonThailand’ssugar industryinordertomeasuretheoverallsustainabilityofthissupplychain(Figure 4).Thesugar industryinThailandisconsideredoneofthemostimportanteconomicpillars.Thailandisthefourth largestsugarproducerandsecondlargestexporterintheworld.Inthisapplication,fourdecision makers(DMs)wereassistedbytheirexperienceinsolvingsuchcasestoevaluatethesustainability ofThailand’ssugarindustry.Theyareexperiencedinmanufacturing(DM1),qualitycontrol(DM2), financeandpurchasing(DM3),andenvironmentalexpert(DM4).Themaingoalofthiscaseisto evaluateThailand’ssugarindustrysupplychainsustainabilitymetricsbasedontheirsignificanceand difficultydegreeofdatagathering.Initially,thefourexpertsidentifiedagroupofsevenrequirements forThailand’ssugarindustrysupplychainsustainabilityevaluation.Theyare:Profit(R1),costs(R2), deliveryreliability(R3),productdevelopment(R4),environmentalaspects(R5),productquality(R6), healthandsecurity(R7).
➢ TherequirementsbytheDMsbasedonlinguisticvariablesinTable 3 areevaluatedasatriangular neutrosophicvalue.TheevaluationisshowninTable 4
➢
AsexplainedinStep2,theplithogenicaggregationoperationisusedtocombinealldecision makersjudgmentsabouttherequirementsbasedonthecontradictiondegreeofeachoneasin Table 5
➢ UsingEquation(6),theaggregationresultsareshowninTable 6 andthentheircrispvalueis foundusingEquation(7).
➢ In Steps3and4,thedistanceofpositiveandnegativeidealsolutionsarefoundusingthe EuclideandistanceasinEquations(8)and(9).Then,theperformancedegreeismeasuredas
➢
mentionedinEquation(10)tofindtheweightvectorofthesevenrequirementsasshownin Table 7.
Thedecisionmakersdefineasetofsupplychainsustainabilitymetricswithrespecttoeconomic, environmental,andsocialdimensions(Table 8).Then,basedonlinguisticvariablesinTable 3, theDMsevaluatethemasspecifiedinTable 9
➢
Table 10 showsthemetricsaggregationusingaplithogenicaggregationoperatoraccordingto eachrequirementandbasedonthecontradictiondegreeinTable 11.Then,theircrispvaluescan befound.
AsinSteps3and4,usingtheEuclideandistanceasinEquations(8)and(9),thedistanceofpositive andnegativeidealsolutionsarefound.Then,asmentionedinEquation(10),theperformance degreeismeasuredtofindtheweightvectorofthemetrics.TheirresultsareshowninTable 12. ➢
➢
AsproposedinOsiro,Lauroetal.2018[11],thelimitationsofneglectingthedifficultyofdata gatheringofeverymetricisaddressed.Inthisstep,thedecisionmakersevaluatethedifficultyin regardstothreedimensions:Informationaccessibility,humanresourcesandtimeneededfor assessments,andotherrequiredresources,asshowninTable 13 ➢
Then,thedecisionmakersevaluationswereaggregatedusingaplithogenicaggregationequation asshowninTable 14: ➢
Equations(8)and(9)wereusedtofindthedistanceofeverymetricfromthepositiveideal solutionandnegativeidealsolution.Then,Equation(10)wasusedtocalculatetheperformance degree,asshowninthefourthcolumninTable 15. ➢ Finally,theperformancescorewasnormalizedusingEquation(11)thatrelatestometrics importanceanddifficultyofdatagathering.ThenormalizationresultsareshowninTable 16 ➢ Figure 5 showstheThailandsugarindustrysupplychainsustainabilitymetricsdistribution categorizedintworegionswhicharetheprioritizedmetricsandlessprioritizedmetricsbasedon thefourdecisionmaker’sevaluation.
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Figure4. Thailand’ssugarindustrysustainabilityrequirementsandmetrics.
Figure 4. Thailand’s sugar industry sustainability requirements and metrics. The requirements by the DMs based on linguistic variables in Table 3 are evaluated as a triangular neutrosophic value. The evaluation is shown in Table 4.
Table 4. The evaluation of the requirements by four DMs. Requirement
Table4. TheevaluationoftherequirementsbyfourDMs.
RequirementDM1 DM2 DM3 DM4 R1 VHHHM R2 HVHMH R3 MHHVH R4 HMMH R5 HVHHM R6 HVHVHM R7 HMHM
Table5. Therequirementscontradictiondegree.
RequirementR1 R2 R3 R4 R5 R6 R7 Contradictiondegree 01/72/73/74/75/76/7
Table6. Theaggregationresultsofrequirements.
RequirementDM1∧p DM2 ∧p DM3 ∧p DM4 CrispValue R1 ((0.29,0.69,1),0.45,0.18,0.36)0.4727 R2 ((0.42,0.69,0.97),0.57,0.18,0.32)0.1664 R3 ((0.56,0.69,0.91),0.68,0.18,0.24)0.6130 R4 ((0.61,0.63,0.8),0.6,0.15,0.12)0.5942 R5 ((0.79,0.69,0.75),0.86,0.18,0.1)0.7192 R6 ((0.92,0.74,0.68),0.9,0.18,0.06)0.7781 R7 ((0.93,0.63,0.43),0.98,0.15,0.01)0.7015
Table7. Theweightsofrequirementsbasedonpositiveandnegativedistances.
Requirement Positive Distance Negative Distance Performance Score WeightRanking
R1 0.23230.408720.63760.15514 R2 0.46740.5380.53510.13025 R3 0.09140.02080.18540.04517 R4 0.10480.03980.27520.06696 R5 0.01460.08520.85370.20772 R6 0.07360.14420.66210.16113 R7 0.00270.06770.96160.23391 total --4.11071
Table8. Economic,environmentalandsocialmetrics.
DimensionMetrics
Economic
Commitmenttocostreduction(I1) Inventoryturnover(I2) Environmentalcosts(I3) Measurementtoolsandmethods(I4) Responsivenesstodemandchange(I5) Manufacturingcost(I6) Deliverycost(I7)
Environmental Wasteminimization(I8)
Airemission(I9) CO2 emission(I10) Recyclingofwaste(I11)
Social Noiselevel(I12) Customercomplaints(I13) Employeetraining(I14) Workingconditions(I15)
Table9. Theevaluationofmetrics.
MetricsR1 R2 R3 R4 R5 R6 R7
I1 VHAMMLML I2 LMHLLML I3 HVHLMVHLH I4 MMHVHHVHH I5 HHVHLVLMVL I6 MVHLVHVLVHVL I7 VHVHVHMVLHL I8 LVHLLVHLVH
I9 VLVLLMVHLVH
I10 VLVLLHVHMVH
I11 MLLVLVHLVH
I12 LLLVLMLH
I13 HLMLMLH
I14 HVHLVHVLHH I15 HHMMLHH
R1 ∧p R2∧p R3∧p R4
Table10. Theaggregationresultsofthemetrics.
R5∧p R6∧p R7
R1 R7 CRISP
I1 ((0.36,0.74,1),0.4,0.13,0.42)((0.1,0.43,0.9),0.29,0.1,0.4)((0.036,0.59,1),0.12,0.25,0.65)0.248
I2 ((0.12,0.49,0.96),0.34,0.13,0.5)((0.15,0.4,0.9),0.3,0.1,0.45)((0.03,0.5,0.99),0.13,0.1,0.69)0.2479
I3 ((0.52,0.48,0.87),0.43,0.15,0.39)((0.6,0.63,0.9),0.5,0.2,0.34)((0.4,0.6,0.97),0.28,0.17,0.54)0.3866
I4 ((0.46,0.7,0.94),0.6,0.15,0.32)((0.6,0.7,0.94),0.7,0.2,0.25)((0.4,0.7,0.97),0.5,0.18,0.41)0.4968
I5 ((0.43,0.63,0.92),0.6,0.18,0.3)((0.1,0.3,0.45),0.5,0.18,0.4)((0.43,0.5,0.8),0.43,0.18,0.46)0.3871
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I6 ((0.55,0.7,0.87),0.56,0.15,0.3)((0.2,0.4,0.62),0.5,0.2,0.37)((0.3,0.6,0.8),0.45,0.18,0.41)0.3953
I7 ((0.78,0.5,0.87),0.88,0.18,0.24)((0.3,0.4,0.7),0.7,0.14,0.24)((0.5,0.45,0.8),0.76,0.16,0.28)0.5191
I8 ((0.5,0.48,0.64),0.6,0.13,0.2)((0.75,0.7,0.8),0.7,0.18,0.2)((0.61,0.6,0.73),0.61,0.16,0.2)0.5484
I6 0.5763 0.4710 I7 0.3569 0.7095
I9 ((0.37,0.73,0.32),0.68,0.15,0.21)((0.8,0.7,0.8),0.7,0.18,0.18)((0.6,0.73,0.53),0.7,0.17,0.18)0.5464
I10 ((0.45,0.38,0.28),0.77,0.18,0.16)((0.9,0.8,0.8),0.8,0.18,0.13)((0.72,0.6,0.48),0.8,0.18,0.12)0.5607
I11 ((0.57,0.4,0.28),0.79,0.2,0.11)((0.9,0.7,0.85),0.8,0.18,0.1)((0.8,0.6,0.46),0.84,0.19,0.07)0.587
I12 ((0.57,0.33,0.29),0.8,0.13,0.09)((0.8,0.6,0.5),0.9,0.15,0.05)((0.78,0.5,0.28),0.9,0.14,0.04)0.5168
I13 ((0.82,0.49,0.3),0.93,0.13,0.57)((0.8,0.6,0.5),0.93,0.15,0.2)((0.9,0.53,0.24),0.97,0.14,0.2)0.5448
I14 ((0.17,0.7,0.46),0.95,0.13,0.02)((0.8,0.6,0.2),0.97,0.2,0.03)((0.8,0.6,0.17),0.99,0.2,0.007)0.5521
I15 ((0.97,0.63,0.4),0.99,0.15,0.003)((0.9,0.6,0.4),0.98,0.2,0.01)((0.99,0.61,0.14),1,0.17,0.001)0.6153
I8 0.4495 0.7418 I9 0.3129 0.5361 I10 0.2911 0.2197 I11 0.7216 0.8233 I12 0.2926 0.6903 I13 0.4316 0.4509 I14 0.6682 0.7648 I15 0.6958 0.595
Table11. Contradictiondegreeofmetrics. MetricsI1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I13 I14 I15 Contradiction degree 0 1 15 2 15 3 15 4 15 5 15 6 15 7 15 8 15 9 15 10 15 11 15 12 15 13 15 14 15
Figure 5 shows the Thailand sugar industry supply chain sustainability metrics distribution categorized in two regions which are the prioritized metrics and less prioritized metrics based on the four decision maker’s evaluation.
Figure 5. The categorization of Thailand’s sugar industry sustainability.
Figure5. ThecategorizationofThailand’ssugarindustrysustainability.
5. Results and Discussion
Based on the decision makers evaluations, the prioritize metrics to evaluate Thailand’s sugar industry sustainability are: Commitment to cost reduction (I1), environmental costs (I3), responsiveness to demand change (I5), manufacturing costs (I6), working conditions (I15) and CO2 emission (I10). In this case, the decision makers were considering the economic aspects more than social or environmental dimensions. The importance of commitment to cost reduction (I1), environmental costs (I3) and recycling of wastes (I11) gained the highest importance in value compared to other metrics, as shown in Figure 6. On the other side, recycling of the waste (I11), employee training (I14) and waste minimization (I8) were the most difficult gathering information metrics. However, the CO2 emission (I10), environmental costs (I3) and responsiveness to demand changes (I5) were the most available information that had the lowest difficulty of data gathering
Table12. Theperformancescorebasedonpositiveandnegativedistances.
MetricsPositiveDistanceNegativeDistancePerformanceScore
I1 0.11140.5139 0.8218
I2 0.45090.11150.1983
I3 0.02720.3753 0.9324
I4 0.26510.1370.3407
I5 0.37480.24670.3969
I6 0.2640.36660.5814
I7 0.38780.24280.385
I8 0.21350.1890.4696
I9 0.41510.21550.3417
I10 0.42940.20120.3191
I11 0.17490.45570.7226
I12 0.38550.1820.3207
I13 0.18540.1540.4537
I14 0.20980.42080.6673
I15 0.08350.2559 0.754
Table13. Theevaluationofinformationgatheringdifficulty.
Metrics Information Availability HumanResource andTime Additional ResourceRequired
I1 HLVH I2 MHH I3 HLL I4 MHH I5 LML I6 HHL I7 HLVH I8 MLVH I9 HML I10 HML I11 MLVH I12 HVHL I13 VHLL I14 HVHVH I15 HHH
Table14. Theaggregationresultsofinformationgatheringdifficulty. MetricsA ∧p B ∧p CCrispValue
I1 ((0.25,0.68,1),0.38,0.18,0.42) 0.4294
I2 ((0.37,0.64,0.96),0.69,0.18,0.25)0.5565
I3 ((0.2,0.4,0.85),0.42,0.13,0.35) 0.3516
I4 ((0.47,0.64,0.92),0.75,0.18,0.2)0.6014 I5 ((0.29,0.42,0.73),0.49,0.1,0.28) 0.3798
I6 ((0.43,0.5,0.76),0.66,0.15,0.2)0.488
I7 ((0.65,0.68,0.83),0.66,0.18,0.22)0.6102
I8 ((0.7,0.67,0.77),0.68,0.15,0.19)0.626 I9 ((0.56,0.49,0.6),0.74,0.13,0.14)0.5094
I10 ((0.6,0.49,0.55),0.78,0.13,0.12)0.506 I11 ((0.82,0.67,0.63),0.8,0.15,0.11) 0.6731
I12 ((0.77,0.55,0.53),0.84,0.15,0.09)0.6013 I13 ((0.78,0.48,0.38),0.83,0.13,0.07)0.5392
I14 ((0.97,0.8,0.72),0.85,0.2,0.04) 0.8124
I15 ((0.94,0.65,0.55),0.99,0.2,0.01 0.7437
Table15. Theperformancescoreofinformationgatheringdifficulty.
MetricsIdealPositiveIdealNegativePerformanceDegree
I1 0.76190.35940.1739
I2 0.69880.63380.352
I3 0.69880.35940.02197
I4 0.69880.63380.2496
I5 0.63380.35940.0743
I6 0.69880.35940.3789
I7 0.76190.35940.6231
I8 0.76190.35940.6624
I9 0.69880.35940.442
I10 0.69880.35940.1003
I11 0.76190.35940.7794
I12 0.76190.35940.601
I13 0.76190.35940.4467
I14 0.76190.69880.6923
I15 0.69880.69880.5
Symmetry 2019, 11,903
Table16. Normalizationofimportanceanddatagatheringdifficulty.
MetricsImportanceDifficultyofDataCollection
I1 0.80310.2762
I2 0.19130.4434
I3 0.87110.1692
I4 0.31190.3429
I5 0.36940.2018
I6 0.57630.4710
I7 0.35690.7095
I8 0.44950.7418
I9 0.31290.5361
I10 0.29110.2197
I11 0.72160.8233
I12 0.29260.6903
I13 0.43160.4509
I14 0.66820.7648
I15 0.69580.595
5.ResultsandDiscussion
Basedonthedecisionmakersevaluations,theprioritizemetricstoevaluateThailand’ssugar industrysustainabilityare:Commitmenttocostreduction(I1),environmentalcosts(I3),responsiveness todemandchange(I5),manufacturingcosts(I6),workingconditions(I15)andCO2 emission(I10).Inthis case,thedecisionmakerswereconsideringtheeconomicaspectsmorethansocialorenvironmental dimensions.Theimportanceofcommitmenttocostreduction(I1),environmentalcosts(I3)and recyclingofwastes(I11)gainedthehighestimportanceinvaluecomparedtoothermetrics,asshown inFigure 6.Ontheotherside,recyclingofthewaste(I11),employeetraining(I14)andwaste minimization(I8)werethemostdifficultgatheringinformationmetrics.However,theCO2 emission (I10),environmentalcosts(I3)andresponsivenesstodemandchanges(I5)werethemostavailable informationthathadthelowestdifficultyofdatagatheringdegreeasinFigure 7 Symmetry 2019, 11, x FOR PEER REVIEW 17 of 21
Figure 6. Metrics evaluation based on their importance Figure6. Metricsevaluationbasedontheirimportance.
Figure 6. Metrics evaluation based on their importance
Figure 7 Metrics evaluation based on difficulty of data collection
Figure7. Metricsevaluationbasedondifficultyofdatacollection.
AstheresultsofdistributionoftheThailandsugarindustrysustainabilitymetricswerecategorized basedonthesignificanceanddifficultyofdatagathering,thisstudyfoundthatthemostpreferred metricswere:Environmentalcost(I3),commitmenttocostreduction(I1)andrealizationtodemand change(I5),respectively,whichwerealleconomicmetrics.Itcanbeconcludedthattheeconomic metricsweremorecriticalthantheenvironmentalandsocialmetrics.Theseresultsarebasedonthe evaluationoffourdecisionmakers,whichmeansthatitisnotageneralresultforsimilarapplications.
As the results of distribution of the Thailand sugar industry sustainability metrics were categorized based on the significance and difficulty of data gathering, this study found that the most preferred metrics were: Environmental cost (I3), commitment to cost reduction (I1) and realization to demand change (I5), respectively, which were all economic metrics. It can be concluded that the economic metrics were more critical than the environmental and social metrics. These results are based on the evaluation of four decision makers, which means that it is not a general result for similar applications.
The main point in this paper is the plithogenic aggregation operation to group the decision maker’s opinions in a more accurate manner. The plithogenic aggregation is taking into consideration the contradiction degree that mainly increases the accuracy of the aggregation. This study aggregated the decision maker’s assessment of supply chain sustainability requirements, the evaluation of metrics importance, and measuring the difficulty of information gathering. Also, the difficulty of data
Themainpointinthispaperistheplithogenicaggregationoperationtogroupthedecision maker’sopinionsinamoreaccuratemanner.Theplithogenicaggregationistakingintoconsideration thecontradictiondegreethatmainlyincreasestheaccuracyoftheaggregation.Thisstudyaggregated thedecisionmaker’sassessmentofsupplychainsustainabilityrequirements,theevaluationofmetrics importance,andmeasuringthedifficultyofinformationgathering.Also,thedifficultyofdatagathering wasnotconsideredinmanyarticles,buttheauthorsfoundittobeacriticaldiminutiontobemeasured. Similarstudiesonthistopicdifferbasedonthemodelandthenatureoftheproblem.AsIgnatius, Joshua,etal.[52]proposed,theANP-QFDapproachwhichmainlyconsideredenvironmentalindicators inordertoensureagreenbuildingstructure.Ontheotherhand,Jamalnia, Aboozar,etal.[53] consideredeconomicmetricssuchascosts,rawmaterialandlabouravailabilitytosolveafacility locationproblemusingtheQFDmethod.Khodakarami,Mohsen,etal.[54]andIzadikhah,etal.[55] useddataenvelopmentanalysis(DEA)fortheevaluationofsupplychainsustainabilitybytakinginto accountmostlyeconomicandenvironmentalmetrics.Ahmadi,etal.[56]usedthebestworstmethod (BWM)toevaluatethesocialsustainabilityofsupplychainbyconsideringsocialindicatorsratherthan economicorenvironmentalaspects.
6.ConclusionsandFutureWorks
Duetostrictgovernmentrequestsandthehugestressfromthepublic,theconsideration ofsupplychainsustainabilitywasincreased[36].Sustainabledevelopmentisoneofthemost significantconditionsofsavingresourcesandkeepingthesupplychainphasesoperatingefficiently[57]. ThisexplainswhySSCMbecameoneofthemostimportantcompetitivestrategiesinorganizations[58]. Thisstudyproposedanefficientcombinationofplithogenicaggregationoperationswiththequality functiondeploymentmethod.Theadvantageofthiscombinationistheimprovementoftheaccuracy oftheresultswhileaggregatingtheassessmentsofthedecisionmakers.QFDproducedgreatresults
insupplychainsustainabilityevaluation.However,itdoesnotconsiderthedepthofdatagathering difficultywhichloosentheaccuracyoftheresults.Thesupplychainsustainabilityevaluationis acriticaltopicthatneedstobestudiedwithhighaccuracybyconsideringeconomic,environmental andsocialdimensions.
Thisstudyobservedtheproposedcombinedmodelinamajorrealworldcasestudy,whichwas Thailand’ssugarindustry.Basedonthenatureofthissupplychainstrategy,itssustainability requirementswasdefinedandmeasuredbyfourdecisionmakers.Theiropinionswereaggregated usingplithogenicaggregationoperatorbasedonthecontradictiondegreetomaximizetheaccuracy oftheaggregation.Inthesameway,themeasurementmetricswasdefinedbasedontheprevious requirementandincludedeconomic,socialandenvironmentaldimensionsevaluatedbytheDMs. Theresultsshowedthattheimportanceofcommitmenttocostreduction(I1),environmentalcosts(I3) andrecyclingofwastes(I11)gainedthemostimportantmetricscomparedtotherestofmetrics.Finally, thedifficultyofdatagatheringofthesemetricswasmeasuredandaggregated.Foreachdimensionof evaluation,thedistanceofthemetricstothebestandworstidealsolutionswascalculatedtofindthe importanceandthedegreeofinformationgatheringdifficulty.Theresultofthispointwasrecyclingof thewaste(I11),employeetraining(I14)andwasteminimization(I8)werethemostdifficultgathering informationmetrics.Moreover,theperformancedegreeofthemetricswasintendedandnormalized todistributethemetricsbasedonthetwodefineddimensions.Itcanbeconcludedthatthemost preferredmetricsbasedinbothdimensionsareenvironmentalcost(I3),commitmenttocostreduction (I1)andrealizationtodemandchange(I5).
Realcontributionsofthisproposedmethodology:
• Themaincontributionofthisproposedmodelliesinprovidingaccurateresultsofthedecision makersassessmentsbasedonthecontradictiondegreewhileapplyingtheaggregation.
• TheplithogenicaggregationoperationallowstheDMstoconsiderseveralexpertsopinionsin ordertomaximizetheefficiencyofthedecisionmaking.
• Also,itmeasuresthesupplychainsustainabilitybasedontwomajoraspects,thesignificanceof themetricsanditslevelofdifficultyofdatagathering,whichisreallyacriticalpointthataffects theevaluations.
• Usingatriangularneutrosophiclinguisticscaletoevaluatetherequirement,metricsand informationavailabilityimprovedthelevelofconsiderationtouncertainty,becauseitconfirms thebestrepresentationbyusingthethreemembershipdegreespositive,negativeandboundary areasofdecisionmaking.
• Theproposedmethodologyisefficientandhasahighaccuracydegreeindecisionmaking problems,Therefore,itisagreattoolthatmayhelpfirmsintheirestimationofcustomerneedsin additiontoevaluatingthesupplychainsustainabilityrequirements.
Infutureresearchdirections,thismodelmaybeusedinassessmentofothersupplychain strategiestoevaluatetheirsustainability.Inaddition,theplithogenicaggregationoperatorscouldbe combinedwithothertechniquestoevaluatethesupplychainsustainability.Finally,moreevaluation dimensionscouldbeaddedtotheimportanceanddifficultyofinformationgatheringtomeasure supplychainsustainability.
AuthorContributions: Allauthorshavecontributedequallytothispaper.Theindividualresponsibilitiesand contributionofallauthorscanbedescribedasfollows:TheideaofthiswholepaperwasputforwardbyM.A.-B. andR.M.,A.E.-N.H.Z.completedthepreparatoryworkofthepaper.F.S.analyzedtheexistingwork.Therevision andsubmissionofthispaperwascompletedbyM.A.-B.
Funding: “Thisresearchreceivednoexternalfunding”.
Acknowledgments: Theauthorswouldliketothanktheanonymousreferees,Chief-Editor,andsupportEditors fortheirconstructivesuspensionsandpropositionsthathavehelpedtoimprovethequalityofthisresearch.
ConflictsofInterest: Theauthorsdeclarenoconflictsofinterest.
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