Integration of fuzzy ANP and fuzzy TOPSIS for evaluating carbon performance of suppliers

14 Pages·2015·0.71 MB·English

by  R. J. Kuo

Int.J.Environ.Sci.Technol.(2015)12:3863–3876
DOI10.1007/s13762-015-0819-9
ORIGINAL PAPER
Integration of fuzzy ANP and fuzzy TOPSIS for evaluating carbon
performance of suppliers
R. J. Kuo1 • C. W. Hsu2,3 • Y. L. Chen4
Received:16July2014/Revised:22March2015/Accepted:21April2015/Publishedonline:12May2015
(cid:2)IslamicAzadUniversity(IAU)2015
Abstract Previous studies have proven that enterprises Keywords Supplier evaluation (cid:2) Fuzzy ANP (cid:2) Fuzzy
addressing green supply chain management problem may TOPSIS (cid:2) Carbon management
produce an apparent improvement to their stockholder in-
terests. According to the report of Kearney (Carbon Dis-
closure Project: supply chain report 2010. CDP, London, Introduction
2010), more than 80 % of the carbon emissions are gen-
erated from the suppliers’ activities in a company’s per- Over the past decade, green supply chain management
formance. Many members of the project claimed that they (GSCM)hasobtainedincreasingattention,yetthegrowing
willreassesstheirsuppliersassoonaspossible.Therefore, importance of GSCM is mainly led by constantly dete-
to combine the carbon management issue and supplier riorating of the environment (Srivastava 2007). However,
evaluating problem turns to be a very crucial work. the practice of GSCM is not only about being kind to the
Therefore,thisstudyintendstodevelopaframeworkofthe environment. As matter of fact, firms that trying to solve
supplier evaluating process for carbon management by problems related to GSCM get apparent improvement on
integrating fuzzy ANP and fuzzy TOPSIS approaches. theirprofit(Sheuetal.2005).ThemainpurposeofGSCMis
Thirteen criteria of carbon management under four di- toreduceenvironmentalpollutionfromupstreamtodown-
mensionswereidentifiedbyliteraturereviewandmodified stream in purchasing raw materials, manufacturing, distri-
according to the opinion of seven experts in the environ- bution,sellingproductsandobsolescingproducts(Kuoetal.
mental department. The model result in a case company 2010).Thus,itindicatesthatthesuppliersplaycriticalroles
showsthatthreeofthemostimportantcriteriaare‘‘carbon infirm’ssuccess.Therefore,firmsshouldembraceasupplier
governance,’’ ‘‘carbon policy’’ and ‘‘carbon reduction tar- evaluation and selection model in determining an appro-
gets.’’ The proposed hybrid methodology has great ability priatelong-termpartnership(Hsuetal.2013).
to explain the vagueness of decision maker’s expression AreportconductedbyKearney(2010)indicatesthatonly
and better discrimination power to evaluate suppliers in 14 % of the greenhouse gas (GHG) emissions are generated
carbon management. from direct operational activities of a company. The rest of
the86 %ofGHGemissionsaregeneratedfromotherindirect
& R.J.Kuo activities,whichmeanstheactivitiesfromthesuppliersinthe
[email protected]
supplychain.Likewise,39 %ofthecarbondisclosureproject
1 DepartmentofIndustrialManagement,NationalTaiwan membersrespondthattheywouldsoondeselectthesuppliers
UniversityofScienceandTechnology, Taipei106,Taiwan, thatdonotmeasureandmanagetheircarbonemissions.Itis
ROC crucial to link carbon management in GSCM with supplier
2 DepartmentofTravelandEcotourism,TungnanUniversity, selectionpracticeassoonaspossible.However,theliterature
NewTaipeiCity222,Taiwan,ROC that addressed carbon management in supplier selection of
3 DepartmentofBusinessAdministration,NationalCentral GSCM is rare (Hsu et al. 2013; Shaw et al. 2012).
University, TaoyuanCity320,Taiwan,ROC The supplier selection issue is one of the multi-criteria
4 SchmidtMarketing(Taiwan)Ltd.,Taipei 100,Taiwan,ROC decision-making (MCDM) problems. This study integrates
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3864 Int.J.Environ.Sci.Technol.(2015)12:3863–3876
two MCDM approaches: analytic network process (ANP) chain management, including product design, material
and technique for order preference by similarity to ideal sourcing and selection, manufacturing processes, delivery
solution (TOPSIS). ANP is applied to determine the rela- of the final product to the consumers and end-of-life man-
tive weights of multiple evaluation criteria, while the agement of the product after its useful life (Srivastava
TOPSISapproachisusedtorankcompetingalternativesin 2007). Green sustainable supply chain management con-
terms of their overall performance. However, these ap- sidersthemanagementofmaterials,informationandcapital
proaches are highly depending on the experiences of the flows as well as cooperation among companies along the
experts. Yet the linguistic expressions of the experts are supply chain while taking goals from all three dimensions
usually with fuzziness or vagueness. Therefore, this study of sustainable development, economic, environmental and
utilizes fuzzy set theory that was proposed by Zadeh social into account, which are derived from customer and
(1965). The theory enables decision makers to handle the stakeholders’ requirements (Seuring andMu¨ller 2008).
ambiguitiesduringthe process ofthelinguistic assessment In GSCM, suppliers and customers work together to
of the data (Bu¨yu¨ko¨zkan and C¸ifc¸i 2012a, b). reduce the environmental impact of production processes
Thefirstobjectiveofthisstudyistoidentifythecriteria and products and to exchange technical information. The
ofsupplierselectionincarbonmanagementwithrespectto firm and the suppliers share a mutual willingness to learn
GSCM.Then,thisstudywilltrytodevelopaframeworkof about each other’s operations and to set goals in order to
thesupplierselectionprocessincarbonmanagementusing limittheirenvironmentalimpact.Awell-integratedGSCM
fuzzy ANP and fuzzy TOPSIS approaches that can be re- has to involve a coordinated flow of materials and infor-
ferred by the application in electronic industry. For the mation between suppliers, manufacturers and customers,
purposeofevaluation,aninternationallywell-knownhigh- and implements product postponement and mass cus-
tech company provided the related data. tomization in the supply chain (Tseng 2011). Shang et al.
The remainder of this paper is organized as follows. (2010) indentified six dimensions of GSCM including
‘‘Literature review’’ section discusses some related back- green manufacturing and packaging, environmental par-
grounds and approaches by reviewing literature. ‘‘Materials ticipation and green marketing, suppliers, stock and eco-
and methods’’ section presents the proposed supplier selec- design. It shows that a greater degree of integration be-
tionandevaluationmodel,whilethemodelevaluationresults tween suppliers and customers in the supply chain results
are illustrated in ‘‘Results and discussion’’ section. Finally, in a greater competitive advantage (Tseng 2011).
theconcludingremarksaremadein‘‘Conclusion’’section.
Supplier selection
Literature review Supplierselectionisoneofthemostimportantcomponents
of production and logistics management for many compa-
This section will discuss some backgrounds related to this nies. The selecting process mainly involves evaluation of
study, including GSCM, supplier selection, ANP and different alternative suppliers based on different criteria.
TOPSIS. This process is essentially considered as a multi-criteria
decision-making (MCDM) problem which is affected by
Green supply chain management different criteria including price, quality, performance,
technical capability and delivery (O¨nu¨t and Soner 2008).
In response to governmental regulations and increasing Thesupplierselectionissuehasbeendiscussedformany
public awareness of the effect of industrial production on years. Due to the governmental regulations and the raising
the environment, organizations start taking major initia- environmental consciousness, the focus of this topic has
tives to transform their supply chain processes. These en- turnedintogreen.Thegreensupplierselectionproblemcan
vironmental issues have received increasing attention in be defined as a classical supplier selection problem in
recent years. Additionally, supply chain operations with which, amongothers,alsoenvironmentalcriteria aretaken
sustainable consideration have also become an important into account in order to select and monitor suppliers’ per-
issue (Bu¨yu¨ko¨zkan and C¸ifc¸i 2012a, b). Environmental formances (Genovese et al. 2010). Rao (2002) described
pollution issue and industrial development are thus com- GSCM as involving screening suppliers based on the en-
bined together with supply chain management and con- vironmental performance of companies and those who
tribute to GSCM (Hsu et al. 2013). meet certain environmental regulations and standards.
The scope of GSCM practices implementation in the Supplier selection in GSCM is clearly a critical activity in
literature ranges from green purchasing to integrated life purchasing management, because firm environmental sus-
cycle management. (Sarkis et al. 2011). GSCM can be de- tainability and ecological performance can be demon-
fined as integrating environmental thinking into supply strated by its suppliers.
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Int.J.Environ.Sci.Technol.(2015)12:3863–3876 3865
The four-step procedure of supplier selection proposed carbon management. These criteria include carbon gover-
by (De Boer et al. 1998) is (1) problem definition, (2) nance,carbon policy, carbonreduction targets, carbon risk
formulation of criteria, (3) qualification and (4) choice. assessment,training-relatedcarbonmanagement,lifecycle
Dickson (1966) conducted a survey and presented 23 dif- cost management, measures of carbon management, in-
ferent criteria to be considered so as to perform an ap- volvement in initiatives for carbon management, manage-
propriate supplier performance evaluation. Hu (2004) ment systems of carbon information, supplier
analyzed 24 papers published after 1991 and discovered collaboration, carbon accounting and inventory, carbon
thatprice,quality,productioncapacityanddeliveryremain verification, and carbon disclosure and report.
as the most important attributes of supplier evaluation Supplier evaluation and selection is a multi-criteria de-
techniques.Tobemorepracticalduringsuppliersselecting, cision-making problem that has been studied extensively,
C¸elebi and Bayraktar (2008) presented 37 criteria and ad- andtherelatedliteraturehasproposedanumberofsupplier
vancedatheorythatintegrationofneuralnetworkanddata selection approaches in the past decade.
envelopment analysis for evaluating suppliers under in- AHP introduced by Saaty (1980) has been applied to
complete information of evaluation criteria. These criteria supplier selection. AHP is designed to handle those deci-
are further divided into four dimensions, cost, quality, sion environments in which subjective judgments are in-
deliveryandservice.Inaddition,someresearchstudiedthe herent in the decision-making process. The advantage of
criteria of green supplier evaluation in order to meet the AHP is to systematically and carefully evaluate the im-
environmental and governmental regulations. Noci (1997) portance of each criterion in relation to the others in a
usedananalytichierarchyprocess(AHP)modeltosupport hierarchical manner. Barbarosoglu and Yazgac (1997) ap-
decision makers in the selection of the most effective pliedanAHPmodeltosolvethesupplierselectionproblem
supplier from an environmental point of view. Four di- for the Turkish electromotor manufacturer. Chan (2003)
mensions were reported including green competencies, presented an AHP with interactive selection model. Inter-
current environmental efficiency, supplier’s green image active selection model is used first to identify interactions
and net life cycle cost. Totally, there are thirteen criteria. and the valid data collection methods for minimizing the
Although Noci (1997) suggested a preliminary frame- effect of qualitative factors. Then, AHP is applied to gen-
work that identifies measures for assessing environmental erate the best possible suppliers. Levary (2008) applied
performance,Humphreysetal.(2003)believedthatonlya AHP approach to evaluate and rank the potential suppliers
little emphasis was placed on environmental cost. Hence, or the so-called reliability chain. However, although AHP
he proposed other more mature criteria for selection pro- canchecktheconsistencyofanindividual’sjudgment,itis
cess. They include the following dimensions: environ- not always achieved a sufficient consistency. Furthermore,
mental pollutant effects cost, environmental improvement AHP is not able to handle the correlation between the
cost, management competencies, green image, design for criteria (Levary 2008). Thus, Saaty (1996) modified AHP
environment, environmental management systems and en- andpresentedanovelapproach,analyticalnetworkprocess
vironmental competencies. (ANP), which is able to consider the correlation between
Green supplier selection problem might be limited in the criteria. ANP was also applied to solve supplier se-
exploring the broad environmental criteria of either quan- lection problems (Bayazit 2006; Gencer and Gu¨rpinar
titative or qualitative property with regard to environment 2007).
cost,productionprocess,productsandmanagementsystem Besides AHP and ANP, DEA was also applied to
inthepastliterature.However,theimportanceofmanaging supplier selection. DEA, which is a mathematical pro-
hazardous substance is increasing recently. Thus, many gramming technique that calculates the relative efficien-
enterprises have tried to consider this issue when deter- cies of multiple decision-making units (DMUs) based on
mining suitable suppliers. Therefore, Hsu and Hu (2009) multiple inputs and outputs yet does not require prior
presented criteria of supplier selection which incorporate assumed weights on inputs and outputs, was introduced
theissueofhazardoussubstancemanagement(HSM).This by Charnes et al. (1978). Liu et al. (2000) introduced a
study considers the following five dimensions: procure- DEA approach for evaluating the overall performance of
ment management, R&D management, process manage- suppliers with a strategic orientation of being able to
ment,incomingqualitycontrolandmanagementsystem.In reduce the number of suppliers. Wu and Blackhurst
addition, firms in different industry sectors begin to rec- (2009) purposed an extended DEA model to evaluate and
ognize carbonissueasoneofthe criticalfactorsinGSCM rank suppliers. The model incorporates a range of virtual
since very high percentage of carbon emissions are pro- standards and weight constraints to enhance the dis-
duced in the total supply chain. However, carbon man- criminatory ability of DEA. Falagario et al. (2012) used
agement in supplier selection is rarely explored. For that a cross-efficiency DEA to solve the decision-making
reason,Hsuetal.(2013)presented13criteriaspeciallyfor problem of supplier selection.
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Decision-making trial and evaluation laboratory The ANP approach is capable of handling interde-
(DEMATEL) is a comprehensive tool for building and pendent relationships between elements by obtaining the
analyzingastructuralmodelinvolvingcasualrelationships composite weights through the development of a ‘‘su-
between complex factors. It was used to solve a group of permatrix.’’ The supermatrix is a two-dimensional
complicated and intertwined problems (Gabus and Fontela matrix:
1973). Hsu et al. (2013) utilized DEMATEL approach to
1. Control hierarchy: It means the interaction between
recognize the influential criteria of carbon management in
criteria and sub-criteria. It includes the goal of the
green supply chain and causal relationships between the
problem, criteria and sub-criteria, each criterion and
evaluationcriteriaofsupplierselection.Changetal.(2011)
sub-criteria are independent at the same level. The
usedfuzzyDEMATELtoevaluatesupplierperformancein
weight of criteria in control hierarchy can evaluate by
electronic industry. Supratik et al. (2012) integrated
AHP approach.
DEMATEL with quality function deployment to select the
2. Network hierarchy: It indicates the relationship be-
most suitable supplier.
tweenelementsandclusters,andcriteriainteraction.It
Besides using a single method for supplier selection,
composes the weights of criteria in control hierarchy
some studies also have employed hybrid methods for sup-
and becomes the supermatrix.
plier selection. Shaw et al. (2012) presented an integrated
approachusingfuzzyAHPandfuzzymulti-objectivelinear The supermatrix must first be reduced to a matrix,
programming for selecting the appropriate supplier in the each of whose columns sums to unity, resulting in a
supply chain addressing the carbon issue. Fuzzy AHP is column stochastic matrix. Interaction in the supermatrix
applied first to analyze the weights of the multiple factors. may be measured according to several different criteria.
Then, they exploit the weights into fuzzy multi-objective To display and relate the criteria, we need a separate
linear programming for supplier selection and quota assig- control hierarchy that includes the criteria and priorities.
nation. Lin (2012) adopted fuzzy ANP and fuzzy multi- For each criterion, a different supermatrix of impacts is
objective linear programming to solve supplier selection developed, and in terms of that criterion, the clusters are
problem. Fuzzy ANP is used to identify the suppliers rank compared according to their relative impact on each
and combine fuzzy multi-objective linear programming to other cluster, thus developing priorities to weight the
select the best suppliers for achieving the optimal order block matrices of eigenvector columns under that cluster
allocation. Liou et al. (2013) proposed a fuzzy integral- in the supermatrix. The result matrix is known as the
based model using DEMATEL combined with ANP and weighted supermatrix. It needs to be stochastic to derive
VIKOR. DEMATEL is applied to determine the effect on meaningful limiting priorities.
each criterion, and ANP was adopted to calculate the ANP has been applied to GSCM in some studies. Hsu
weights of the criteria. Finally, VIKOR are applied to and Hu (2009) used an ANP to select suppliers incorpo-
transform the performance values intoweighed gaps. rating the issue of GSCM with respect to 19 criteria. Cri-
teria of HSM competence were considered the
Applications of ANP to green supply chain requirements of hazardous substance for environmental
management regulations. Dou et al. (2013) introduced a gray ANP
model to identify green supplier development programs
Saaty (1996)purposedtheANP tosurmount thelimitation (GSD) that would improve suppliers’ performance. The
ofAHP.ThelimitationofAHPisthatitdoesnotallowthe modelappliedANPtocalculatetheweightsofcriteriaand
elements of hierarchical model to have dependence and rankofGSDprograms.Then,thegrayaggregationmethod
feedback between each involved element. ANP allows in- was used to evaluate suppliers’ involvement propensity in
terdependence interaction between involved elements that various GSD programs. Bu¨yu¨ko¨zkan and C¸ifc¸i (2011) de-
can be criteria and alternatives. ANP is not limited by the veloped an approach for sustainable supplier selection
independentassumptionhappenedbetweenthecriteriaofa based on fuzzy ANP considering incomplete preference
decision and the alternatives of that decision, or simply relations.Criteriawereevaluatedbyexperts’opinions,and
among the criteria or among the alternatives themselves. missing values were estimated by incomplete preference
As a consequence, the importance of criteria determines relations. Then, fuzzy ANP was applied to identify the
the importance of the alternatives. That is, the preference optimal supplier. In addition, Bu¨yu¨ko¨zkan and C¸ifc¸i
of one element may be changed depending on the control (2012a, b) also proposed a hybrid model for evaluating
criterion. Further, ANP shows the interdependencies with green suppliers. Relation dependence from fuzzy DEMA-
feedback in the structure which has two-way arrows and TEL was applied to fuzzy ANP. Then, fuzzy ANP could
connected cycles of its clusters like a network instead of obtain weights of the criteria. Finally, fuzzy TOPSIS was
level hierarchy in AHP. used to rank the alternatives.
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Int.J.Environ.Sci.Technol.(2015)12:3863–3876 3867
Applications of TOPSIS to green supply chain performance of firms. Thus, it is an important part in the
management GSCM. This section proposes a fuzzy MCDM method for
green supplier selection illustrated in Fig. 1. It consists of
TOPSIS was presented by Hwang and Yoon (1981) to three steps:
solvemulti-criteriadecision-making(MCDM)problems.It
1. The Delphi method for determining the criteria,
improvedtheconceptofdisplacedidealsolutionpresented
2. Fuzzy ANP method for obtaining the weights of each
by Zelany (1974). It has been proved to be one of the best
criterion and
MCDM methods in addressing the rank reversal issue,
3. Fuzzy TOPSIS method for ranking the alternatives.
which is the change in the ranking of alternatives when a
non-optimalalternativeisintroduced(Zanakisetal.1998). Basically, the Delphi method is used to determine the
The basic idea of TOPSIS method is that the most op- important criteria first by domain experts. Then, these
timalalternativeshouldhavetheshortestdistancefromthe important criteria are applied to develop the fuzzy ANP
positive ideal solution and the longest distance from the structure. From fuzzy ANP, it can provide the weight for
negativeidealsolution(ChenandHwang1992;Hwangand each criterion. These weights will be used by fuzzy TOP-
Yoon 1981). This method assumes that every criterion is SIS in order to make the supplier selection. The detailed
monotoneincreasingormonotonedecreasing.Thepositive discussion for each step is as follows.
ideal solution stands for a solution that maximizes the
benefit criteria and minimizes the cost criteria, while the Delphi method
negative ideal solution minimizes the benefit criteria and
maximizes the cost criteria (Opricovic and Tzeng 2004). This step intends to determine the most significant criteria
That is, the positive ideal solution is composed of all best and collect the corresponding factors that are considered
values attainable from the criteria, and the negative ideal forthe greensupplierselection.Consideringthe criteria of
solution consists of all worst values attainable from the greensupplierselectionincarbonmanagementisanarrow
criteria (Wang and Chang 2007).
TOPSIS was also applied to GSCM. However, TOPSIS
Literature review
was not used individually. It was always integrated with
(Collect criteria for green
other methods. Azadnia et al. (2012) introduced an inte- supplier selection)
grated approach to solve sustainable supplier selection
problem. Fuzzy AHP was applied to obtain the weights of
1. Delphi method
criteria for sustainable supplier selection. Then, self-orga-
(Criteria determination for supplier selection in
nizing map neural network was utilized to cluster the carbon management)
suppliers based on their scores regarding each criterion.
Finally, TOPSIS was used twice to rank the suppliers 2. Fuzzy ANP 3. Fuzzy TOPSIS
within the best cluster. Kannan et al. (2013) presented an
integrated approach for selecting the green suppliers and Fuzzy ANP Fuzzy TOPSIS
questionnaire questionnaire
allocating the optimum order quantities. Fuzzy AHP and
TOPSIS were used to assign criteria weight and rank all
alternative suppliers. Then, a weighted max–min fuzzy
Super Decisions
Derive the positive
multi-objective model was constructed to determine the calculation
ideal solution(PIS)
orderquantity.Objectiveofthemodelwastomaximizethe and Negative ideal
total value of purchasing and to minimize the total cost of solution(NIS)
purchasing. Govindan et al. (2013) introduced a fuzzy
Consistency test
approach for supplier selection decisions with consid- No CR≤ 0.1 Calculate the relative
eration of sustainability criteria based on the triple bottom closeness of each
alternatives toward
line concept. Fuzzy TOPSIS was used to aggregate the Yes
PIS and NIS
ratings and generate an overall performance score.
Obtain the
Weight of
criteria
Materials and methods
Rank the preference
order of alternatives
‘‘Literature review’’ section has demonstrated that the
supplier selection procedure would straightly affect the
environmental sustainability and the ecological Fig.1 FuzzyMCDMsupplierselectionframework
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3868 Int.J.Environ.Sci.Technol.(2015)12:3863–3876
field, and it would need professionals to get involved. wherefA~:X !½0;1(cid:3).isthemembershipfunctionofAand
Delphi method therefore becomes the first choice. Delphi
fA~ðXiÞ stands for the membership degree of xi in A~.
method is adopted to collect data from a series of expert
The related definitions are described as follows.
panels that consist of purchasing managers or appropriate
people for related industries or academics. Descriptions of Definition 1 When X is a continuous rather than a
the steps are listed below: countable or finite set, the fuzzy set A~ is denoted as
1. Literature research. A~¼ fA~ðxiÞ(cid:6)ðxÞ, where x2X.
2. Assemble an expert panel.
Definition2 WhenXisacountableorfiniteset,thefuzzy
3. Questionnaire design: The form of questionnaire uses
Likert scale to exhibit importance of each criterion. set A~ is represented as A~¼PifA~ðxiÞ(cid:6)ðxÞ, where xi2X.
The importance and their scores can be divided into
five levels: ‘‘very important, 5’’; ‘‘important, 4’’; Definition3 AfuzzysetA~oftheuniverseofdiscourseX
‘‘general, 3’’; ‘‘unimportant, 2’’; and ‘‘very unimpor- is normal when its membership function fA~ðxÞ satisfies
tant, 1.’’ max f~AðxÞ¼1.
4. Pretest and distribute questionnaire.
Definition 4 A fuzzy number is a fuzzy subset in the
5. Analyze the first-round answers.
universeofdiscourseXthatisnotconvexbutalsonormal.
6. Design and distribute the second-round questionnaire
withtheresultofthefirst-roundquestionnaire,experts Definition 5 The fuzzy a-cut A~ and strong a-cut A~aþ of
are encouraged to change their previous answers
thefuzzysetA~intheuniverseofdiscourseXaredefinedby
referring others’ answers.
7. Analyze the second-round answers and delete criteria A~a¼(cid:4)xijfA~ðxiÞ(cid:4)a;xi2X(cid:5); wherea2½0;1(cid:3)
ð2Þ
with a lower score. A~aþ¼(cid:4)xijfA~ðxiÞ(cid:4)a;xi2X(cid:5); wherea2½0;1(cid:3)
8. Determine the green supplier selection criteria.
Definition6 AfuzzysetA~oftheuniverseofdiscourseX
isconvexifandonlyifeveryA~aisconvex,thatis,A~aisa
Fuzzy ANP
close interval of R. It can be written as
In order to measure the relationship or the relative im- A~a¼½P1ðaÞ;P2ðaÞ(cid:3); wherea2½0;1(cid:3) ð3Þ
portance between criteria determined by the expert panel,
Definition 7 A triangular fuzzy number (TFN) can be
this step utilizes a fuzzy ANP approach to obtain the
definedasatriplet(a1,a2,a3);themembershipfunctionof
weights of each criterion through pair comparison ana-
the fuzzy number A~ is defined as:
lysis by experts.
Fuzzy theory was developed by Zadeh (1965) to solve 8
0; x(cid:5)a1
pwrhoibclhemarsewimitphredceissec,riupnticoenrstaionfoarcftuivziztyiesinadnedciosibosne-rmvaatkioinngs >>>>><ðx(cid:6)a1Þ; a1(cid:7)x(cid:7)a2
f ðxÞ¼ a2(cid:6)a1 : ð4Þ
problem related to complicate systems. The evaluation A~ ða3(cid:6)xÞ
goifveancbeyrtaeixnpeorbtsjeocrt disecaislwioanysmaekxeprrsesosnedquinaliltiantgivueisctirciteerxia- >>>>>:0a;3(cid:6)a2; xa2(cid:8)(cid:7)ax3(cid:7)a3
pressions instead of crisp values, based on experience and
Let A~ and B be two TFN parameterized by the triplet
expertise. Such linguistic evaluations are vague, which
(a1, a2, a3) and (b1, b2, b3), respectively, and then, the
makesfurtheranalysisishardtocompute.Therefore,fuzzy
operational laws of these two TFNs are as follows:
set theory can be implemented to measure ambiguous
conceptsassociatedwithhuman’ssubjectivejudgments.In
A~ðþÞB~¼ða1;a2;a3ÞðþÞðb1;b2;b3Þ¼ða1þb1;a2þb2;a3þb3Þ
fuzzy logic, each number between 0 and 1 indicates a
A~ð(cid:6)ÞB~¼ða1;a2;a3Þð(cid:6)Þðb1;b2;b3Þ¼ða1(cid:6)b1;a2(cid:6)b2;a3(cid:6)b3Þ
partial truth, whereas crisp sets correspond to binary logic
A~ð(cid:9)ÞB~¼ða1;a2;a3Þð(cid:9)Þðb1;b2;b3Þ¼ða1b1;a2b2;a3b3Þ
[0,1] (Lin 2013).
Someimportantdefinitionsoffuzzysettheoryarelisted A~ð(cid:10)ÞB~¼ða1;a2;a3Þð(cid:10)Þðb1;b2;b3Þ¼ða1=b1;a2=b2;a3=b3Þ
below (Lin 2013). Let X be the universe of discourse and ð5Þ
X = {x1,x2,x3,…,xn}.AfuzzysetA~ofXisasetoforder
Since fuzzy set theory is similar to human being’s
pairs:
thinking, it was integrated with ANP and TOPSIS. The
(cid:4)(cid:2)x1;fA~ðx1Þ;ðx2;fA~ðx2ÞÞ;ðxn;fA~ðxnÞÞ(cid:3)(cid:5); ð1Þ fuzzy ANP contains the following steps:
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Int.J.Environ.Sci.Technol.(2015)12:3863–3876 3869
Step 1: Construct ANP network framework. When method (Bu¨yu¨ko¨zkan and C¸ifc¸i 2012a, b; O¨nu¨t and Soner
constructing the network of ANP, we assemble an expert 2008) as follows:
panel with five managers in risk management and envi- W~ ¼(cid:2)wl;wm;wu(cid:3); k¼1;2;...;m ð12Þ
ronmental safety and health (ESH) management depart- k k k k
ment from industry and two environmental science hQn asi1=n
professors to discuss the ANP framework. ws ¼ j¼1 kj ; s2fl;m;ug ð13Þ
Step 2: Design and distribute fuzzy ANP questionnaire. i Pn hQn ami1=n
i¼1 j¼1 ij
This study applies the method proposed by Chen and
Hwang(1992)anddesignsfivesemanticwordsets(equally Then,thecenterofgravityisemployedtodefuzzifythe
important, fairly important, important, very important and fuzzy weights as follows:
extremely important) to collect the responses of expert (cid:9)(cid:2)wu(cid:6)wl(cid:3)þ(cid:2)wm(cid:6)wl(cid:3)(cid:10)
panel on each question. Then, transform these 1–9 scales F ¼ i i i i þwl; ð14Þ
i 3 i
into TFN as shown as follows:
where F represents a weight of ith criterion in the fuzzy
1. Equally important: 1 = (1,1,1), i
pair comparison matrix.
2. Middle point: 2 = (1,2,3),
After calculation, the results are fed to Super Decisions
3. Fairly important: 3 = (2,3,4),
softwaretogeneratetheinitialsupermarketsforcalculating
4. Middle point: 4 = (3,4,5),
the relative weights.
5. Important: 5 = (4,5,6),
Step 4: Consistence test: If A[B and B[C, we can
6. Middle point: 6 = (5,6,7),
infer that A[C. This is called ‘‘transitive law’’ in
7. Very important: 7 = (6,7,8),
mathematics. After establishing the pairwise comparison
8. Middle point: 8 = (7,8,9) and
and evaluate eigenvector, it is necessary to check whether
9. Extremely important: 9 = (8,9,10).
the ‘‘consistency ratio’’ (CR) of pairwise compare matrix
Equation (6) is used to perform calculations. conforms the transitive law. CR and CI are defined as
(cid:7) (cid:8) (cid:7) (cid:8) follows:
a~ij ¼ 1=n (cid:11) a~1ij(cid:11)a~2ij(cid:11)(cid:2)(cid:2)(cid:2)(cid:11)a~nij ; ð6Þ CI
CR¼ ; and ð15Þ
where a~ is the integrated TFN, and A~n is the response of RI
ij ij
nth expert for ith criterion toward jth criterion. It can be CI¼kmax(cid:6)n: ð16Þ
represented as TFN as follows: n(cid:6)1
(cid:9) (cid:10) CIisconsistencyindex,RIisrandomindex,andnisthe
a~ ¼ l ;m ;u ; ð7Þ
ij ij ij ij
number of criteria.
h i
Pn lk . 8
lij ¼ k¼1 ij n; ð8Þ <CI¼0; Fullcompliancewithconsistency
CI[0; Notinfullcompliancewithconsistency
h i
Pn mk . :CR(cid:7)0:1; Acceptableconsistency
mij ¼ k¼1 ij n; and ð9Þ
h i If the consistency ration is not in full compliance, it is
Pn uk .
uij ¼ k¼1 ij n; ð10Þ necessary to correct or adjust the result from the pairwise
comparison.
where l represents the lower bound of TFN, m represents
themiddleboundofTFN,andurepresentstheupperbound Fuzzy TOPSIS
of TFN.
Step3:Establishapairwisecomparisonmatrixandobtain After obtaining the weight ofeach criterion by fuzzy ANP
therelativeweights.ThisstudyintroducesTFNstorepresent approach, the next step is to adopt the fuzzy TOPSIS
the semantic ranking of expert panel and integrates the method as the ranking method for the alternatives. The
measurementofimportanceforallquestionresults. fuzzy TOPSIS method also introduces TFN and linguistic
2a~ (cid:2)(cid:2)(cid:2) a~ 3 variable with five semantic word sets (very high, high,
11 1j
A~¼64 ... ... ... 75 ð11Þ mpeerdtsiuomn,elaocwhqaunedstvioerny.Tlohwe)futozzcyoTllOecPtStIhSemreestphoondsecoonfsiesxts-
a~ (cid:2)(cid:2)(cid:2) a~
i1 ij of following steps (Chen 2000):
The corresponding fuzzy weight of each comparison Step 1: Construct the fuzzy decision matrix D~ for m
(cid:2) (cid:3)
matrix is calculated by the logarithmic least squares criteria for n alternatives: x~ ¼ a ;b ;c represents the
ij ij ij ij
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3870 Int.J.Environ.Sci.Technol.(2015)12:3863–3876
transformed TFNs from linguistic terms. D~ is represented D(cid:6)
RC(cid:12) ¼ j ; j¼1;2;...;n: ð28Þ
as follows: j D(cid:12)þD(cid:6)
j j
2 3
x~ (cid:2)(cid:2)(cid:2) x~ Step 7: Rank the preference order: The index values of
11 1n
D~ ¼64 ... ... ... 75 ð17Þ RC(cid:12)i lie between 0 and 1. The larger index value means
closer to a positive ideal solution for alternatives.
x~ (cid:2)(cid:2)(cid:2) x~
m1 mn
Step 2: Calculate the normalized fuzzy decision matrix
Results and discussion
R~ as follows:
R~¼(cid:9)r~(cid:10) : ð18Þ After presenting the proposed hybrid MCDM method for
ij m(cid:9)n green supplier selection, this section will employ a case
The normalized r~ is calculated as: company to demonstrate the proposed method. The case
ij
" # studyisbasedonanelectroniccompanythatisapioneerin
a b c
r~ ¼ ij; ij; ij ð19Þ the LEDs industry. As one of the largest technology com-
ij c(cid:12) c(cid:12) c(cid:12)
j j j panieslistedontheTaiwanStockExchange,thecasecom-
panycommittedtodevelopeco-friendlymanufacturingand
c(cid:12) ¼maxc : i¼1;2;...;m; j¼1;2;...;n: ð20Þ
j ij focusedoncooperatingwithgreensuppliersinTaiwan.
Toavoidtheeffectofoutliers,TOPSISneedstonormalize
the decision matrix. Criteria for supplier selection in carbon
Step 3: Calculate the weighted normalized decision management
matrix V~: In this study, the weights are obtained from
In this study, there are two steps for constructing supplier
previous fuzzy ANP approach. The weighted normalized
selection criteria undercarbonmanagement practice.First,
value v~ is calculated as follows:
ij
collect green supplier selection criteria from the previous
V~ij ¼wirij; i¼1;2;...;m; j¼1;2;...;nand ð21Þ literature and then implement the Delphi method. The
V~¼(cid:9)v~ (cid:10) ; ð22Þ questionnaireusesaLikertscaletoshowtheimportanceof
ij min each criterion. The importance can be divided into five
wherewiistheweightoftheithcriterionandPmi¼1wi ¼1. levels: ‘‘very important, 5’’; ‘‘important, 4’’; ‘‘general, 3’’;
Step 4: Determine the fuzzy positive ideal solution A(cid:12) ‘‘unimportant, 2’’; and ‘‘very unimportant, 1.’’ The ques-
and fuzzy negative ideal solution A(cid:6). tionnaire for Delphi method was distributed to seven ex-
perts in the area of environmental science for different
A(cid:12) ¼(cid:2)v~(cid:12)1;v~(cid:12)2;...;v~(cid:12)n(cid:3) ð23Þ industriesandtotwoexpertsfromacademy.Aftertworun
A(cid:6) ¼(cid:2)v~(cid:6);v~(cid:6);...;v~(cid:6)(cid:3) ð24Þ of survey, the result already has been consistent.
1 2 n
According to the results of the questionnaires, four cri-
where v(cid:12) ¼ð1;1;1Þandv(cid:6) ¼ð0;0;0Þ; j¼1;2;...;n. teria ‘‘establishment of carbon emission factor,’’ ‘‘carbon
j j
Step 5: Calculate the distance of each alternative using disclosure and report,’’ ‘‘energy management system (ISO
A(cid:12) and A(cid:6) as follows: 50001),’’ ‘‘information system’’ and ‘‘supplier collabora-
tion’’ are deleted. In addition, one criterion ‘‘risk assess-
m
D(cid:12) ¼Xd(cid:2)v~ ;v~(cid:12)(cid:3); j¼1;2;...;n ð25Þ ment for low carbon requirement’’ is added. Finally, there
j ij i
i¼1 are13criteriaforsupplierselectionincarbonmanagement
as shown in Table 1.
m
D(cid:6) ¼Xd(cid:2)v~ ;v~(cid:6)(cid:3); j¼1;2;...;n ð26Þ
j ij i
i¼1 Generate criteria weights by ANP
where
After settling down the criteria for the supplier selection
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi model, the expert panel is gathered to build the ANP
d(cid:2)A~;B~(cid:3)¼ 1hða1(cid:6)b1Þ2þða2(cid:6)b2Þ2þða3(cid:6)b3Þ2i: structure. Since the pairwise comparison process will be
3
performed using Super Decisions software, the experts
ð27Þ
need to be asked to describe the dependences and feed-
Step 6: Calculate the relative closeness to the positive backs between each involved criterion and dimension.
ideal solution: The relative closeness of the alternative A Throughthisprocess,anANPmodelwithfeedbacksystem
i
with respect to A(cid:12) is defined as: is constructed as shown in Fig. 2.
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Int.J.Environ.Sci.Technol.(2015)12:3863–3876 3871
Table1 CriteriaforsupplierselectionincarbonmanagementwithrespecttoGSCM
Dimensions Criteria Explanation
Organizational Carbongovernance(C1) Supplierhasaresponsibledepartmenttohandlecarbonmanagement
management Carbonpolicy(C2) Supplierhasexplicitpolicyorplanaboutcarbonmanagement
Carbonreductiontargets(C3) Supplierhassetcleargoalsforcarbonemissionreductiontarget
GHGverification(ISO14064)(C4) SupplierachievesISO14064regulation
Riskassessmentforlowcarbonrequirement Supplierhasariskassessmentforlowcarbonrequirementfrom
(C5) governmentorherclients
Training-relatedcarbonmanagement(C6) Supplierimplementscarbonmanagementtrainingtoitsstaffs
Processmanagement Availabilityanduseoflowcarbon Whethertoapplylowcarbontechnologiesornot
technologies(C7)
Energyefficiency(C8) Electricityconsumptionisloworefficientofsuppliers
Measuresofcarbonreduction(C9) Supplierismonitoringandmeasuringitscarbonemissionquantity
Procurement Availabilityofacarbonsupplierselection Supplierhasitsownreliablesupplierselectionsystem
management system(C10)
Requirementoflowcarbonpurchasing Supplierhaslowcarbonregulationforitsrawmaterialpurchasing
(C11)
R&Dmanagement Capabilityoflowcarbondesign(C12) Assessingwhethersupplierhastheabilitytodesignlowcarbonproduct
Inventoryofcarbonfootprintofproduct Supplierimplementscarbonfootprintinventoryandcalculating
(C13)
Fig.2 ANPmodel
Then, the pairwise comparison questionnaires are pair comparison matrix can be generated. Table 2
designed based on the ANP mode. According to the shows the pair comparison matrix for one of the
pair comparison result gathering from the experts, the experts.
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3872 Int.J.Environ.Sci.Technol.(2015)12:3863–3876
In this step, the consistency of questionnaire results After generating the defuzzified weights of every fuzzy
(CI B 0.1)hastobetestedinadvance.Then,transformthe pair comparison matrix, all the weights are imported back
expert’s judgment into TFNs. We can obtain the fuzzy in Super Decisions software again to run the final weights
judgment matrix as shown in Table 3. for each selected supplier selection criteria with respect to
By using a fuzzy number merging method, the carbon management.
fuzzy judgments of all experts can be integrated and InSuperDecisionssoftware,itcanautomaticallyrunthe
an integrated matrix can be generated as shown in ANP process and generate the supermatrix. By assigning
Table 4. thedefuzzifiedweightsdirectlytoeachcomparisonmatrix,
Table2 Pairedcomparisonmatrixbetweendimensions
Organizationalmanagement Processmanagement Procurementmanagement R&Dmanagement
Organizationalmanagement 1.000 2.000 4.000 7.000
Processmanagement 0.500 1.000 2.000 5.000
Procurementmanagement 0.250 0.500 1.000 3.000
R&Dmanagement 0.143 0.200 0.333 1.000
Table3 Fuzzypairedcomparisonmatrixbetweendimensions
Organizationalmanagement Processmanagement Procurementmanagement R&Dmanagement
Organizationalmanagement (1.000,1.000,1.000) (1.000,2.000,3.000) (3.000,4.000,5.000) (6.000,7.000,8.000)
Processmanagement (4.000,5.000,6.000) (1.000,1.000,1.000) (1.000,2.000,3.000) (4.000,5.000,6.000)
Procurementmanagement (0.200,0.250,0.333) (0.333,0.500,1.000) (1.000,1.000,1.000) (2.000,3.000,4.000)
R&Dmanagement (0.125,0.143,0.167) (0.167,0.200,0.250) (0.250,0.333,0.500) (1.000,1.000,1.000)
Table4 Integratedfuzzy
Eigenvectors Normalizedfuzzyweights Defuzzifiedweights
pairedcomparisonmatrix
betweendimensions Organizationalmanagement (2.034,2.599,3.163) (0.387,0.495,0.602) 0.494
Processmanagement (1.103,1.413,1.766) (0.210,0.269,0.336) 0.272
Procurementmanagement (0.542,0.736,0.946) (0.103,0.140,.180) 0.141
R&Dmanagement (0.420,0.509,0.627) (0.080,0.097,0.119) 0.099
Table5 Weightedsupermatrix
Goal C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13
ofsupplierselectionincarbon
management Goal 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C1 0.09 0.00 0.23 0.23 0.00 0.38 0.50 0.00 0.00 0.00 0.60 0.40 0.00 0.00
C2 0.09 0.23 0.00 0.18 0.00 0.39 0.31 0.00 0.00 0.00 0.25 0.25 0.00 0.00
C3 0.11 0.20 0.26 0.00 0.00 0.00 0.00 1.00 1.00 0.82 0.00 0.20 0.69 0.65
C4 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35
C5 0.13 0.29 0.24 0.00 0.00 0.00 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C6 0.03 0.08 0.08 0.07 0.00 0.23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C7 0.10 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C8 0.14 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C9 0.03 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00
C10 0.08 0.14 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 0.00
C11 0.06 0.06 0.04 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.15 0.00 0.00 0.00
C12 0.08 0.00 0.00 0.15 0.00 0.00 0.00 0.00 0.00 0.18 0.00 0.00 0.00 0.00
C13 0.02 0.00 0.00 0.03 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
123