Multiple Criteria Decision Making with Fuzzy Sets: MS Excel® and Other Software Solutions

215 Pages·2022·3.274 MB·English

by  Babek Erdebilli, Gerhard-Wilhelm Weber

Multiple Criteria Decision Making
Babek Erdebilli
Gerhard-Wilhelm Weber   Editors
Multiple Criteria
Decision Making
with Fuzzy Sets
MS Excel® and Other Software Solutions
Multiple Criteria Decision Making
SeriesEditor
Constantin Zopounidis, School of Production Engineering and Management,
TechnicalUniversityofCrete,Chania,Greece
This book series focuses on the publication of monographs and edited volumes of
wideinterestforresearchersandpractitionersinterestedinthetheoryofmulticriteria
analysis and its applications in management and engineering. The book series
publishes novel works related to the foundations and the methodological aspects
of multicriteria analysis, its applications in different areas in management and
engineering, as well as its connections with other quantitative and analytic disci-
plines. In recent years, multicriteria analysis has been widely used for decision
makingpurposesbyinstitutionsandenterprises.Researchisalsoveryactiveinthe
field,withnumerouspublicationsinawiderangeofpublicationoutletsanddifferent
domains such as operations management, environmental and energy planning,
financeandeconomics,marketing,engineering,andhealthcare.
(cid:129)
Babek Erdebilli Gerhard-Wilhelm Weber
Editors
Multiple Criteria Decision
Making with Fuzzy Sets
®
MS Excel and Other Software Solutions
Editors
BabekErdebilli Gerhard-WilhelmWeber
DepartmentofIndustrialEngineering PolitechnikaPoznańskaWydziałInżynierii
AnkaraYıldırımBeyazıtUniversity Zarządzania
Ankara,Turkey Poznań,Poland
ISSN2366-0023 ISSN2366-0031 (electronic)
MultipleCriteriaDecisionMaking
ISBN978-3-030-98871-5 ISBN978-3-030-98872-2 (eBook)
https://doi.org/10.1007/978-3-030-98872-2
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Preface
ItgivesmegreatpleasuretoannouncethepublicationofthebookMultipleCriteria
Decision-MakingwithFuzzySets.Itfeaturesthirteenchaptersandinterestingpapers
writtenbyresearcherswithcompetenceinfuzzysetsandmultiplecriteriadecision-
making(MCDM).Ineverydaylife,thereareseveralexamplesofMCDMproblems:
in general, clearly considering multiple factors results in more informed and better
decisions. MCDM models are typically highly complicated, and it is frequently
required to use the decision-maker’s preferences to distinguish between various
solutionsandfindtheoptimumcompromise.Makingachoiceisanintegralelement
ofdailylife.Makingchoicesisanecessarycomponentofourdailylife.Theprimary
issue is that nearly all choice problems involve several, frequently contradictory,
factors. There has been a huge amount of research into how to tackle such chal-
lenges.Asdiverseasthesechallengesare,theyfallundertwomajorcategories:
(cid:129) MultipleAttributeDecision-Making(MADM)
(cid:129) MultipleObjectiveDecision-Making(MODM)
From apracticalpoint ofview,MADMisconnectedwithissuesthat haveaset
numberofpossibilities.TheDecision-Maker(DM)istaskedwiththeresponsibility
of selecting/prioritizing/ranking a finite number of alternative courses of action.
MODM, on the other hand, is not connected with problems whose solutions are
predetermined. The DM’s principal objective is to provide the “most” promising
optionpossiblegivenrestrictedresources.TomanageproblemsinvolvingMultiple
Attribute Decision-Making and to comprehend uncertainty and the vagueness of
humanbeings,MCDMandFuzzyMCDMmathematicalmethods.
MCDM is often regarded as a comprehensive decision-making technique that
combines both quantitative and qualitative variables into the decision-making pro-
cess.Inrecentyears,anumberoffuzzyMCDMalgorithmshavebeenpresentedfor
the purpose of selecting the optimal solutions. Fuzzy set theory and MCDM were
bothdevelopedintheearly1970s,whichcombinedwiththedevelopmentoffuzzy
settheory.Ithasbeenshownthatthetheoryiswell-suitedtodecision-making,which
v
vi Preface
has resulted in the development of a large number of novel techniques for fuzzy
multi-criteriadecision-making.
ThisbookprovidesanoverviewoffuzzyMCDMcategorytheoryanddiscusses
someofitsoldestandmostrecentapplications.Numerousreal-worldexampleswere
offered to illustrate the concept of category in fuzzy MCDM and its applications.
Despite its success in a variety of knowledge fields, MCDM is still imperfect at
combining imprecise, vague, and incomplete data. The flexibility, dynamic, and
receptive nature of MCDM enables a plethora of new possibilities for utilizing
decision theory. When Bellman and Zadeh, followed by Zimmermann a few years
later,addedfuzzysetsintotheequation,theypavedthewayforanewcategoryof
decision procedures capable of addressing situations that were previously inacces-
sible or unsolvable using normal MCDM techniques. When fuzzy set theory was
incorporatedintoMCDMresearch,themethodologiesfollowedasimilarpath.The
first category of fuzzy MCDM includes a variety of methods for ranking. This
comprisesthedegreeofoptimality,theHammingdistance,thecomparisonfunction,
thefuzzymeanandspread,theproportiontotheideal,theleftandrightscores,the
centroidindex,theareameasurement,andlinguisticrankingalgorithms.Thesecond
category consists of strategiesthat make use of a number of techniques inorder to
calculate the relative importance of various characteristics and alternatives. The
majority of the methods mentioned in this category were aimed at determining the
weightofvariousobjects.Fuzzysimpleadditiveweighingmethods,fuzzyanalytic
hierarchy processes, fuzzy conjunctive or disjunctive weighting methods, fuzzy
outranking methods, and max-min weighting methods are some of the approaches
covered. Fuzzy mathematical programming is the third category, and it is also the
onetowhichthemostofcontributionshavebeenmade.
Thefollowingarethemaintopicspresentedandanalyzedinthisspecialvolume:
Multi-Criteria Group Decision-Making Under Heterogeneous Information,
q-rung orthopair fuzzy-based MCDM, integrated intuitionistic Fuzzy MCDM,
FuzzyMCDM,interval type-2fuzzy MCDM,Interval-ValuedPythagorean Fuzzy,
HesitantFuzzyMCDM,anextensionMCDM,Reflectingbarrierandfuzzydemands
and applications to Environment, Health care, economics, Industry, engineering,
management,andsocialsciences.
The Chapter “Data-Driven Multi-Criteria Group Decision Making Under Het-
erogeneous Information” as a result of Nurullah Güleç and Özgür Kabak’s discus-
sion of complicated decision-making (GDM) challenges involving various
stakeholders, interest in group decision-making methodologies continues to grow.
The success of GDM processes is directly tied to the decision-makers’ (DMs)
evaluations. When DMs contribute differently due to their knowledge, experience,
orconsistencywithoneanother,theyareassignedweightstoreflecttheirvalueinthe
finaloutput.Accordingtothem,acumulativebeliefdegree(CBD)strategybasedon
beliefstructureandfuzzylinguistictermsisofferedtodealwithsuchheterogeneous
informationinstances.Todeterminethefinalranksofthealternatives,theinforma-
tion provided in intuitionistic fuzzy numbers, hesitant fuzzy linguistic terms, and
hesitantfuzzynumbersisconvertedtobeliefdegrees.
Preface vii
InChapter“SupplierEvaluationwithQ-RungOrthopairFuzzy-BasedCOPRAS
Method”, Adem Pınar highlights the importance of developing long-term business
relationships in supply chain management, specifically evaluating and selecting an
appropriate supplier. Introduce a new modification of the Complex Proportional
Assessment (COPRAS) method based on q-Rung Orthopair Fuzzy Sets that suc-
cessfully handle the uncertainty associated with real-world subjective decisions.
COPRAS takes into account the influence of maximizing criteria and the total of
theweightednormalizedvaluesofminimizingcriteria,whicharebasicallycostand
benefitcriteria.
InChapter“AnIntegratedIntuitionisticFuzzyMCDMModel:ItsApplicationto
RIS”, Babek Erdebilli and Adel Hatami-Marbini demonstrate how to construct a
hybrid model using the intuitionistic fuzzy TOPSIS (IFTOPSIS) technique, data
envelopment analysis (DEA), and the analytical hierarchy process (AHP).
IFTOPSIS is used to handle more difficult issues in which the decision-maker is
unsure or hesitant to assign qualitative preference ratings to the objects being
evaluated. In this chapter, integration of models that can benefit from both tech-
niques’ strengths and flaws is discussed extensively. Using the model provided in
this work has various advantages, the most significant of which is the capacity to
makeaccurateassessmentsontheperformanceofdecision-makingunits(DMUs).
In Chapter “Prioritization of Automotive Dealers According to Environmental
Sustainability Criteria Using Fuzzy EDAS Method”, Burcu Elmas, Gülin Feryal
Can, and Mamak Ekinci provide an evaluation using the Distance from Average
Solution (EDAS) method, one of the MCDMs, in combination with fuzzy logic to
establishdealerrankings.Thismethodtakestheaveragesolutionintoaccountwhen
determining the optimal alternative. The EDAS technique, which compares the
alternatives’ negative and positive distances to the average solution, is useful
becauseitmayrankthealternativesusingtwodistinctreferencepoints.Thepurpose
of incorporating EDAS with fuzzy logic is to simulate uncertainty in the measure-
ment values of many criteria that affect dealers’ environmental sustainability per-
formancelevelsandtorelatethisuncertaintytodealers’priorities.
In Chapter “Ranking of the Bottled Water Brands Using Interval Type-2 Fuzzy
ELECTRE Method”, Mükerrem Bahar Başkir and Pelin Toktaş emphasize the
importance of Type-1 fuzzy sets and their primary memberships for handling
uncertainty in the traditional ELECTRE. The interval type-2 fuzzy ELECTRE
method was used in this study to rate bottled water brands according to five
analyticalcriteria.Ithasbeendeterminedthatthemostappropriatebrandalternative
outperformstheremainingbrandsintermsofmeetingeachcostcriterion.
InChapter“Interval-ValuedPythagoreanFuzzyEntropyWeightMethodandIts
ApplicationtoSupplierSelection”,ElifHaktanirandCengizKahramanintroducean
EntropyWeightMethod(EWM)forweighingcriteriaviavaluedispersionmeasure-
ments.Thegreaterdispersionanddegreeofdifferentiationinthismethodimplythat
it contains more information. In MCDM with some degree of uncertainty, humans
often prefer linguistic assessments. This uncertainty is addressed in this chapter
usinginterval-valuedPythagoreanfuzzy(IVPF)sets.TheIVPFweightedgeometric
(IVPFWG)operatorisusedtoaggregatethejudgmentsofseveraldecision-makers.
viii Preface
InChapter“HospitalPerformanceEvaluationinCOVID-19PandemicbyUsing
Hesitant Fuzzy MABAC”, Yavuz Selim Özdemir and Nihan Çağlayan emphasize
theneedofevaluatinghospitals’intensivecareunitperformanceduringtheCOVID-
19pandemicusingtheHesitantFuzzyMABAC(Multi-AttributiveBorderApprox-
imationAreaComparison)approach.Therearenumeroussetsofcriteriaforevalu-
ating hospitals in the literature. The technical competence of the COVID-19
emergency service, patient satisfaction, health personnel sufficiency, and patient
follow-upprocedurecriteriawereusedinthischapter.
InChapter“AMultipleCriteriaRankingMethodBasedonOutrankingRelations:
An Extension for Prospect Theory”, Esra Karasakal, Orhan Karasakal, and Hazel
Şentürk Prospect Theory is integrated into a well-known multiple criteria ranking
approach, PROMETHEE. PROMETHEE investigates the outranking relations
among options based on the preference functions. Prospect Theory examines the
options with a difference function based on profits and losses. The preference
functions of PROMETHEE are modified to capture the choosing behavior of the
decision-maker.ThesuggestedmethodisageneralizationofPROMETHEE.
InChapter“DevelopmentofaCuttingInsertSelectionModelUsingtheHesitant
FuzzyTOPSIS”,YusufTanselİç,KumruDidemAtalay,andEsraDinlerdeveloped
a cutting tool selection model for the turning process based on a hesitant Fuzzy
Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS). They
categorized several well-known cutting tool inserts into a hesitant valued choice
matrixbasedontheuncertaintyassociatedwiththeevaluationofthecriteriavalues.
TheythenprocessedtheTOPSISmodel’shesitantstepstowardsolvingthecutting
toolselectionproblem.
In Chapter “Comparing SMEs According to Industry 4.0 Adaptations for Miti-
gating the Bullwhip Effect”, Babek Erdebilli, Emine Nur Nacar, Mete Gündoğan,
andSujanPiyaintroduceanIntuitionisticFuzzyTOPSISforsupplychainmanage-
mentevaluation.Theterm“Industry4.0”isgainingpopularity.Transparencyacross
thesupplychainhasbeendemonstratedtobecritical,ashassuccessfulcooperation
betweensuppliers,manufacturers,distributors,wholesalers,retailers,andcustomers.
However,conflictsbetweensupplychainenterprisesareunavoidablethroughoutthe
flowofinformationfromonestagetothenext,withnegativeconsequencesforthe
entirechain.
In Chapter “Evaluating Industry 4.0 Barriers by Intuitionistic Fuzzy VIKOR
Method”, Ibrahim Yilmaz presents an Intuitionistic fuzzy VIKOR technique for
identifying the most and least essential barriers that organizations may encounter
duringtheIndustry4.0transitionprocess.Industry4.0isincreasinglydemonstrating
its effects today, as technology advancements accelerate. Working and daily life
continue to evolve as a result of technological advancements. As a result of the
changes, new business lines, job prospects, and opportunities develop. Due to the
advancements brought about by Industry 4.0, firms may now reach new levels of
efficiency, especially in terms of manufacturing, developing, and distributing their
products.
In Chapter “An Application of Interval Valued Pythagorean Fuzzy WASPAS
Method for Drone Selection to Last Mile Delivery Operations”, Ahmet Aktas,
Preface ix
Mehmet Kabak an analytic model based on Interval-Valued Pythagorean Fuzzy
Weighted Aggregated Sum Product Assessment (IVPF-WASPAS) method is pro-
posedtodeterminethemostsuitabledronealternative.Therearenumeroustypesof
drones, each with a unique capacity and function. For last mile delivery firms,
selecting the best appropriate drone is a strategic decision. The applicability of the
suggested methodology is proved in this study through a case study including the
evaluationoffouralternativedronesusingfivecriteria.Alternativedronesarelisted
inaggregatedperspectivesofrelativeimportance.
InChapter“StationaryCharacteristicsforRenewal-RewardProcesswithGener-
alized Reflecting Barrier and Fuzzy Demands”, Basak Gever and Tahir Khaniyev
describearewardprocesswithbarriersasavaluabletoolthatiscommonlyusedin
quantumphysicsandinavariety ofengineering challenges. Some issues ininven-
tory theory, in particular, can be modeled using renewal-reward processes with a
generalized reflecting barrier. However, in inventory models, for example, when
predictingthedistributionofrandomvariablessuchasdemandorinterarrivaltimes,
theentiredistributionorpartofitsparametersmaybefuzzyasaresultofimprecise
information or subjective evaluations. Thus, when the demands are fuzzy random
variables, an inventory model is examined using a renewal-reward process with a
generalizedreflectingbarrier.
Ankara,Turkey BabekErdebilli
Poznań,Poland Gerhard-WilhelmWeber