Handbook of Operations Analytics Using Data Envelopment Analysis

511 Pages·2016·6.08 MB·English

by  Shiuh-Nan Hwang

International Series in
Operations Research & Management Science
Shiuh-Nan Hwang
Hsuan-Shih Lee
Editors
Joe Zhu
Handbook of
Operations
Analytics Using
Data Envelopment
Analysis
International Series in Operations Research
& Management Science
Volume 239
SeriesEditor
CamilleC.Price
StephenF.AustinStateUniversity,TX,USA
AssociateSeriesEditor
JoeZhu
WorcesterPolytechnicInstitute,MA,USA
FoundingSeriesEditor
FrederickS.Hillier
StanfordUniversity,CA,USA
More information about this series at http://www.springer.com/series/6161

Shiuh-Nan Hwang • Hsuan-Shih Lee • Joe Zhu
Editors
Handbook of Operations
Analytics Using Data
Envelopment Analysis
Editors
Shiuh-NanHwang Hsuan-ShihLee
DepartmentofBusiness CollegeofMaritimeScience
Administration andManagement
MingChuanUniversity NationalTaiwanOceanUniversity
Taipei,Taiwan Keelung,Taiwan
JoeZhu
SchoolofBusiness
WorcesterPolytechnicInstitute
Worcester,MA,USA
ISSN0884-8289 ISSN2214-7934 (electronic)
InternationalSeriesinOperationsResearch&ManagementScience
ISBN978-1-4899-7703-8 ISBN978-1-4899-7705-2 (eBook)
DOI10.1007/978-1-4899-7705-2
LibraryofCongressControlNumber:2016941328
©SpringerScience+BusinessMediaNewYork2016
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or
dissimilarmethodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexempt
fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthis
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained
hereinorforanyerrorsoromissionsthatmayhavebeenmade.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerScience+BusinessMediaLLCNewYork
Preface
In recent years, handbooks on Data Envelopment Analysis (DEA) have been
published. They include Handbook on Data Envelopment Analysis (eds,
W.W. Cooper, L.M. Seiford, and J. Zhu, 2011, Springer), Data Envelopment
Analysis: A Handbook of Modeling Internal Structures and Networks (eds,
W.D.CookandJ.Zhu,2014,Springer),DataEnvelopmentAnalysis:AHandbook
of Models and Methods (ed. J. Zhu, 2015, Springer), and Data Envelopment
Analysis: A Handbook of Empirical Studies and Applications (ed. J. Zhu, 2016,
Springer).Itis well known thatDEAis a“data-oriented” approach for evaluating
the performance of a set of entities called decision-making units (DMUs) whose
performance is categorized by multiple metrics. These performance metrics are
classifiedortermedasinputsandoutputs.Ingeneral,DEAfindsanenvelopmentfor
asetofdata.Thisenvelopmentiscalledefficientfrontier(inproductiontheory)or
best-practice frontier (in benchmarking terminology). While many DEA applica-
tionscanbeviewedasestimationofproductionfunctions,DEAcanalsobeapplied
tomanufacturingaswellasserviceoperationswhereDEAisusedasaversatiletool
formakingvariousoperationaldecisions.
TocomplementtheexistingDEAhandbooks,thecurrenthandbookfocuseson
DEA applications in operations analytics which are fundamental tools and tech-
niquesforimprovingoperationfunctionsandattaininglong-termcompetitiveness.
Infact,thechaptersinthehandbookdemonstratethatDEAcanbeviewedasData
EnvelopmentAnalytics.
Chapter1,byRuizandSirvent,reviewscross-efficiencyevaluationwhichpro-
videsapeerappraisalinwhicheachDMUisevaluatedfromtheperspectiveofall
oftheothersbyusingtheirDEAweights.
Chapter 2, by Zhou, Poh, and Ang, presents a case study on measuring the
environmentalperformanceofOECDcountries.Environmentalperformancemea-
surement provides an analytical foundation for environmental policy analysis and
decisionmaking.
v
vi Preface
Chapter 3, by Serrano-Cinca, Mar-Molinero, and Fuertes Calle´n, demonstrates
how to select a set of performance metrics (inputs and outputs) in DEA with an
applicationtoAmericanbanks.
Chapter 4, by Liu, proposes using a relational network model to take the
operations of individual periods into account in measuring efficiencies, and the
inputandoutputdataaretreatedasfuzzynumbers.
Chapter5,byAvkiranandZhu,showshowtheefficientfrontiermethodsDEA
and stochastic frontier analysis (SFA) can be used synergistically. As part of the
illustration, the authors directly compare locally incorporated foreign banks with
Chinesedomesticbanks.
Chapter 6, by de la Torre, Sagarra, and Agasisti, integrates DEA and
multidimensional scaling, with the aim to discuss the potential complementarities
andadvantagesofcombiningbothmethodologiesinordertorevealtheefficiency
frameworkandinstitutionalstrategiesoftheSpanishhighereducationsystem.
Chapter 7, by Wu, Kweh, Lu, Hung, and Chang, constructs a dynamic three-
stage network DEA model which evaluates the R&D efficiency, technology-
diffusion efficiency, and value-creation efficiency of Taiwanese R&D organiza-
tionsovertheperiod2005–2009.
Chapter 8, by Wanke and Barros, presents a bootstrapping-based methodology
toevaluatereturnstoscaleandconvexityassumptionsinDEA.
Chapter9,byLozano,Hinojosa,Marmol,andBorrero,studiesthepossibilities
of hybridizing DEA and cooperative games. Specifically, bargaining games and
transferableutilitygames(TUgames)areconsidered.
Chapter 10, by Fukuyama and Weber, uses DEA to represent the production
technology and directional distance functions to measure bank performance. The
performance measure allows the researcher to compare observed inputs and out-
puts, including undesirable outputs, with the outputs and inputs that might be
producedif aproducer were abletooptimallychoose productionplansrelativeto
adynamicbenchmarktechnology.
Chapter 11, by Ke, presents an input-specific Luenberger energy and environ-
mental productivity indicator. DEA is utilized to estimate the directional distance
function for composing the Luenberger energy and environmental productivity
indicator.
Chapter12,byMehdiloozadandSahoo,addressestheissueofreferencesetby
differentiatingbetweentheuniquelyfoundreferenceset,calledtheglobalreference
set (GRS), and the unary and maximal types of the reference set for which the
multiplicity issue may occur. The authors propose a general linear programming-
based approach that is computationally more efficient than its alternatives. The
authorsdefinethereturnstoscaleofaninefficientDMUatitsprojectionpointthat
isproducedbyall—butnotsome—oftheunitsinitsGRS.
Chapter13,byLim,Jahromi,Anderson,andTudori,evaluatesandcomparesthe
technological advancement observed in different hybrid electric vehicle (HEV)
market segments over the past 15 years. The results indicate that the introduction
ofawiderangeofmidsizeHEVsisposingathreattothetwo-seatersandcompact
Preface vii
HEV segments, while an SUV segment shows a fast adoption with a significant
performanceimprovement.
Chapter14,byDing,Feng,andWu,providesradialmeasurementsofefficiency
fortheproductionprocesspossessingmulticomponentsunderdifferentproduction
technologies. Their approach is based on the construction of various empirical
productionpossibilitysets.Thentheauthorsproposeaprocedurethatisunaffected
bymultipleoptimaforestimatingreturnstoscale.
Chapter15,byHarrisonandRouse,considersissuesaroundtheuseofaccount-
inginformationinDEAwithsuggestionsonhowaccountingdatacanbeusedinthe
modeling process and how DEA can be combined with other accounting
approachestoimproveperformanceevaluation.
Chapter16,bySueyoshi,explainshowtouseDEAenvironmentalassessmentto
establishcorporatesustainabilityanddiscussesthatenvironmentalassessmentand
protectionareimportantconcernsinmodernbusiness.
Chapter17,bySueyoshiandYuan,summarizespreviousworksontheresearch
efforts,includingconceptsandmethodologies,onDEAenvironmentalassessment
appliedtoenergyinthepastthreedecades.
Chapter18,bySarkis,providesanoverviewofDEAandhowitcanbeutilized
aloneandwithothertechniquestoinvestigatecorporateenvironmentalsustainabil-
ity questions. Some future DEA directions that could be used for research and
applicationincorporateenvironmentalsustainabilityarealsodefined.
Wehopethatthishandbook,alongwithotheraforementionedDEAhandbooks,
canserveasareferenceforresearchersandpractitionersusingDEAandasaguide
for further development of DEA. We thank reviewers who provided valuable
suggestions and comments to the chapters. We are also grateful to the authors
whomakeimportantcontributionstowardadvancingtheDEAresearchfrontier.
References
Cook, W. D., & Zhu, J. (2014). Data envelopment analysis: A handbook of modeling internal
structuresandnetworks.Springer.
Cooper,W.W.,Seiford,L.M.,&Zhu,J.(2011).Handbookondataenvelopmentanalysis(2nd
ed.).Springer.
Zhu,J.(2015).Dataenvelopmentanalysis:Ahandbookofmodelsandmethods.Springer.
Zhu, J. (2016). Data envelopment analysis: A handbook of empirical studies and applications.
Springer.
Taipei,Taiwan Shiuh-NanHwang
Keelung,Taiwan Hsuan-ShihLee
Worcester,MA,USA JoeZhu

Contents
1 RankingDecisionMakingUnits:TheCross-Efficiency
Evaluation................................................... 1
Jose´ L.RuizandInmaculadaSirvent
2 DataEnvelopmentAnalysisforMeasuring
EnvironmentalPerformance.................................. 31
PengZhou,KimLengPoh,andBengWahAng
3 InputandOutputSearchinDEA:TheCaseofFinancial
Institutions................................................... 51
C.SerranoCinca,C.MarMolinero,andY.FuertesCalle´n
4 Multi-periodEfficiencyMeasurementwithFuzzyData
andWeightRestrictions...................................... 89
Shiang-TaiLiu
5 PitchingDEAAgainstSFAintheContextofChinese
DomesticVersusForeignBanks............................... 113
NecmiKemalAvkiranandYushu(Elizabeth)Zhu
6 AssessingOrganizations’EfficiencyAdoptingComplementary
Perspectives:AnEmpiricalAnalysisThroughData
EnvelopmentAnalysisandMultidimensional
Scaling,withanApplicationtoHigherEducation.............. 145
EvaM.delaTorre,MartiSagarra,andTommasoAgasisti
7 CapitalStockandPerformanceofR&DOrganizations:
ADynamicDEA-ANPHybridApproach...................... 167
Yueh-ChengWu,QianLongKweh,Wen-MinLu,
Shiu-WanHung,andChia-FaChang
ix