A Fuzzy Multi Criteria Approach for Evaluating Sustainability Performance of Third Party Reverse Logistics Providers

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1 A Fuzzy Mult Crtera Approach for Evaluatng Sustanablty Performance of Thrd Party Reverse Logstcs Provders Nadne Kafa, Yasmna Han, Abederrahman Mhamed To cte ths verson: Nadne Kafa, Yasmna Han, Abederrahman Mhamed. A Fuzzy Mult Crtera Approach for Evaluatng Sustanablty Performance of Thrd Party Reverse Logstcs Provders. Bernard Grabot; Bruno Vallespr; Samuel Gomes; Abdelazz Bouras; Dmtrs Krtss. IFIP Internatonal Conference on Advances n Producton Management Systems (APMS), Sep 2014, Ajacco, France. Sprnger, IFIP Advances n Informaton and Communcaton Technology, AICT-439 (Part II), pp , 2014, Advances n Producton Management Systems. Innovatve and Knowledge-Based Producton Management n a Global-Local World. < / _33>. <hal > HAL Id: hal Submtted on 26 Oct 2016 HAL s a mult-dscplnary open access archve for the depost and dssemnaton of scentfc research documents, whether they are publshed or not. The documents may come from teachng and research nsttutons n France or abroad, or from publc or prvate research centers. L archve ouverte plurdscplnare HAL, est destnée au dépôt et à la dffuson de documents scentfques de nveau recherche, publés ou non, émanant des établssements d ensegnement et de recherche franças ou étrangers, des laboratores publcs ou prvés. Dstrbuted under a Creatve Commons Attrbuton 4.0 Internatonal Lcense

2 A fuzzy mult crtera approach for evaluatng sustanablty performance of thrd party reverse logstcs provders Nadne KAFA, Yasmna HANI, Abederrahman EL MHAMEDI Equpe MGSI/ LISMMA Unversté de Pars8 140, rue de la nouvelle France, Montreul, France (n.kafa, y.han, Abstract. Due to the complexty and specfcty of reverse logstcs system, some organzatons outsource all or part of the reverse logstcs process to thrd party provder (3PRLP). The selecton of the most effcent 3PRLP s a crucal task n whch t s mportant to take nto account envronmental and socal crtera as well as economc crtera owng to economc nterests, stakeholder pressures, and envronmental legslatons. The te between all three aspects of sustanablty n 3PRLP selecton problem has been almost gnored. Ths research work deals wth ths ssue and develops a new ntegrated approach for selectng the best sustanable 3PRLP. A hybrd mult-crtera makng decson model s structured to assgn the prorty weghts of decson crtera usng fuzzy analytc herarchy process (FAHP) and to get the fnal rankng of provders usng fuzzy preference rankng organzaton method for enrchment evaluaton (F- PROMETHEE). A numercal example s also presented to llustrate the proposed approach. Keywords: Sustanable supply chan, reverse logstcs, thrd-party provder selecton, fuzzy AHP, fuzzy PROMETHEE. 1 Introducton Reverse logstcs (RL) s defned as a set of elements (collecton, sortng, treatment, nformaton system and dstrbuton system) [1] deals wth product returns n order to retreve sustanable values. In today's compettve envronment, dfferent organzatons take nto consderaton the management of reverse flows n ther supply chan system. Accordng to Kannan et al. [2] dealng wth returns s a complcated ssue because of the need of specalzed nfrastructures, the lack of experence, and consderable uncertantes regardng to delvery, qualty and quantty of the returned products. Therefore, many organzatons decde to outsource reverse logstcs functon to thrd-party provders (3PRLPs). On the other hand, the ntroducton of corporate socal responsblty prncples and sustanablty ssues n RL system s a means of developng a complete sustanable performance model [3].

3 Several studes have dscussed sustanablty ssues and hghlghted the mportance of achevng the trple bottom lne of economc, socal, and envronmental goals n conventonal supply chan management [4], green supply chan management [5], reverse logstcs [3], and suppler selecton [6], whle researches consder all three dmensons of sustanable development n 3PRLP selecton are rather lmted tll now. Wthn supply chan management, the mplementaton of sustanable ntatves and green practces s dffcult wthout cooperaton wth all partners n the network. Therefore, selectng rght 3PRLPs based on ntegrated sustanablty crtera can assst organzatons to mprove ther sustanablty performance. Some of the 3PRLP selecton related papers have started payng more attenton to ths ssue. Wang and Zhu [7] proposed a model to solve the problem of selectng an approprate 3PRLP n envronmental vewpont of low-carbon economy. Tajk et al.[8]developed a hybrd fuzzy AHP-TOPSIS approach for evaluatng sustanable 3PRLPs. The framework they proposed s hghly stylzed. However, there s a real need for research on 3PRLPs selecton problem wth sustanablty concerns because the majorty of the present models focuses on economc and slghtly on envronmental factors. Ths paper s among the frst research works that shed lght on ths ssue. Hence, the man contrbuton of ths paper s to propose a new model for mult-crtera 3PRLP selecton problem based on fuzzy AHP-PROMETHEE approach nvolvng sustanablty crtera. The next secton presents the hybrd fuzzy AHP-PROMETHEE approach to assess sustanablty performance of 3PRLPs. Thrd secton presents an llustratve example and results analyss. Fnally, the paper ends wth conclusons. 2 Proposed model for 3PRL provder selecton Due to 3PRLP selecton problem s a complcated MCDM problem and tme consumng assgnment, a clear process should be requred to resolve t. The proposed model s structured to allocate the prorty weghts of decson crtera by resolvng FAHP algorthm and to rank 3PRL provders by resolvng F-PROMETHEE algorthm, as llustrated n Fg. (1). 2.1 Sustanablty decson crtera The key crtera for selectng sutable 3PRLP can typcally nclude cost, qualty, and fnancal performance. Furthermore, ndexes of low-carbon and socal ndcators can play a crucal role n selectng 3PRLPs as the need to coordnate and ntegrate all the busness functons wth sustanablty consderatons. Reverse logstcs outsourcng should clearly be economcally, envronmentally and socally appled. The sustanablty crtera and sub-crtera have been defned n detal based on specfc lterature revew n the area of 3PRL provder selecton by Kafa et al.[9].

4 Defnng sustanablty crtera Selectng the effcent 3PRL provder among «n» alternatves Eco Overall costs (Eco₁) Global qualty (Eco₂) RL Costs (R₁) Cost of relatonshp (R₂) Qualty of product (R₃) Qualty of servce (R₄) Qualty of the people (R₅) Reverse logstcs capacty (Eco₃) Fnancal capablty (R₆) Specalzed nfrastructures (R₇) Sklled manpower (R₈) Green level (Env₁) Envronmental management (R9) Polluton (R₁₀) Resource consumpton (R₁₁) Env RL practces (Env₂) Organzatonal role of RL(Env₃) Collecton (R₁₂) Sortng (R₁₃) Treatment (R₁₄) Redstrbuton (R₁₅) Recyclng (R₁₆) Reuse (R₁₇) Remanufacturng (R₁₈) Dsposal (R₁₉) Soc Mcro-socal mpact (Soc₁) Macro-socal mpact (Soc₂) Employees satsfacton (R₂₀) Customers satsfacton (R₂₁) Stakeholders satsfacton (R₂₂) Health and safety (R₂₃) Local communty (R₂₄) Aspects Crtera Sub-crtera Fuzzy AHP for assgnng weghts to the decson elements Fuzzy PROMETHEE for rankng the 3PRLPs Fg. 1. Proposed fuzzy AHP-PROMETHEE model for selectng the best 3PRL provder 2.1 Fuzzy AHP algorthm The basc concept of AHP method [10] s to model a general decson problem as a herarchcal structure ncludng sub-problems that can be easly evaluated n order to determne the prortes va par-wse comparson of the elements at each level of the decson herarchy. Ths method has ganed popularty observng the amount of studes that have utlzed t ncludng supply chan management. As provders selecton

5 crtera always contan ambguty and dversty of meanng, the fuzzy AHP algorthm proposed by Kwong and Ba [11] was employed for estmatng the weghts of selected crtera as follows. Step 1: Construct the fuzzy judgment matrces (FCMs) for each level. Fuzzy theory set [12] s ncorporated wth par-wse comparson n AHP to get more beneft of human reasonng that s approxmate rather than precse. The decson maker (DM) preference s represented n form of fuzzy par-wse comparson matrx (FCM) usng the lngustc fuzzy scale represents n terms of trangular fuzzy numbers (TFN) (equally=, moderately=, strongly=, very strongly=, extremely= ). 1 a~ a~... a~ n 1 j ~ ~ ~ : a~ j 1, 3, 5, ~ 1 ~ 1 1, 3, j j ~ ~ ~ ( ~ a a a n ~ ~ ~ A FCM aj) 7, 9 ~ 1 ~ 1 ~ , 7, 9 ~ ~ ~ a... 1 n1 an2 an3 Step 2: Determne the prortes of dfferent decson elements. The prortzaton of the elements of each matrx s done by solvng fuzzy egenvalues and egenvectors. A fuzzy egenvalue ~ s a fuzzy number soluton to: A ~~ x ~ ~ x (1) : x ~ s a non-zero n 1 fuzzy egenvector contanng fuzzy numbers. The nterval arthmetc and the α-cuts are ntroduced to perform fuzzy multplcaton and addton. By defnng the confdence level α n order to ntegrate the decson maker (DM) confdence over the judgments [13], the TFN can be llustrated as: a, c ( b a) a, ( c b c 0,1 TFN ) (2) For example, wth respect to the level, the lower lmt and upper lmt of the fuzzy numbers and, can be obtaned by applyng equaton (2). ~ ~ 1 3 = [1 + 2α, 5 2α], 3 = [1/ (5 2α), 1/ (1 + 2α)] The degree of satsfacton for the judgment matrx à s estmated from the DM by the ndex of optmsm. The larger value of the ndex s the hgher degree of optmsm that s defned as a lnear convex combnaton [13]: ~ ~ (1 ) ~ a a a 0,1 (3) j ju The egenvector x ~ s calculated by fxng and substtutng the mal egenvalue λ nto the equaton (1). Step 3: Calculate the consstency rato of a matrx à usng the mathematcal formula CR= CI/RI as explaned by Saaty [10]. A CR of 10% or less s acceptable. Step 4: Normalze weght vector n order to determne the local and total mportance weghts (W). Followng the above explaned steps, the MATLAB package s used to calculate the prorty weghts of the man aspects, the crtera, and the sub crtera, (see Table 1.) and then the results are analyzed n the thrd secton. jl

6 2.1 Fuzzy PROMETHEE algorthm The preference rankng organzaton method for enrchment evaluaton (PROMETHEE) s one of the conventonal smple outrankng methods to resolve MCDM problems whch s appled n varous areas of research. Ths method was developed by Brans and Vncke [14] based on the explotaton of a valued outrankng relaton va par-wse comparsons between the alternatves regardng dfferent crtera. The evaluaton table that shows the performance level of each potental alternatve for each crteron s requred as the frst pont to apply PROMETHEE method. In ths paper, all alternatves (3PRLPs) are evaluated usng lngustc scale wth correspondng fuzzy numbers (TFN) [very good=(7,7,9), good= (5,7,9), far =(3,5,7), poor = (1,3,5), very poor=(1,1,3)].the defuzzfcaton of TFNs s done usng graded mean ntegraton representaton (GMIR) method, proposed by Chen and Hseh [15].Where TFN= (a, b, c), the GMIR R(TFN) of TFN s: R( TFN) 1/ 6 ( c 4a b) (4) Addtonal nformaton on prorty weghts of the crtera s also requred whch s done usng FAHP method n ths study, as PROMETHEE method cannot dstrbute weghts to the crtera. The varous steps of PROMETHEE can be outlned brefly as follows: Step 1: Calculate preference functon of alternatve (a) wth regard to alternatve (b) n the set of A alternatves for each sub-crteron: d ( a, b) P ( a, p) 0,1 P ( a, p) f (5) Where d ( a, b) s the dfference between the evaluaton of a and b on th subcrteron. Several basc preference functons are explaned by Brans and Vncke [14] lke V-crteron, Gaussan crteron, and U-crteron. In ths study, the usual functon has been selected whch s mostly used wth qualtatve crtera [16]. Step 2: Calculate outgong flow and ncomng flow for each alternatve usng equaton (6) and equaton (7) for rankng the alternatves by a partal preorder PROMETHEE I technque that ntroduces the ncomparablty between alternatves. Then calculate net flow usng equaton (8) for rankng the alternatves by a total preorder PROMETHEE II technque. ( a ) 1/( A 1) W P ( a, x) (6) xa 1 ( a ) 1/( A 1) W P ( x, a) (7) xa 1 ( a) ( a) ( a) (8) The Vsual PROMETHEE software ncludes the basc of PROMETHEE method s used to calculate outrankng flows and rank the alternatves.

7 3 Applcaton and dscusson of results The problem dscussed here s related to a manufacturng company wants to mplement reverse logstcs actvtes by outsourcng them to 3PRLP. The company desres to consder all the possble key factors whch can affect the effcency of reverse logstcs functons. Furthermore, t s mportant to consder socal and envronmental as well as economc attrbutes n 3PRLP selecton process n order to acheve sustanable compettve advantage. Currently, the company has four alternatves, namely RLP₁, RLP₂, RLP₃ and RLP₄. To select the rght one, the proposed model s appled. The relatve mportance of each par of selected crtera s prortzed after askng the global logstcs manager as decson maker (DM) n the company concerned to answer a questonnare ncludng all possble par-wse comparson. Table 1. The prorty weghts of decson crtera Aspects Weghts Local weghts Total weghts Crtera Sub- Crtera Local weghts Total weghts Consstency ECO (0,64) ENV (0,22) SOC (0,14) Eco₁ 0,5076 0,3267 R₁ 0,5858 0,1914 R₂ 0,4142 0,1353 Eco₂ 0,4014 0,2583 R₃ 0,1159 0,0299 R₄ 0,5294 0,1368 R₅ 0,3547 0,0916 Eco₃ 0,091 0,0585 R₆ 0,2156 0,0126 R₇ 0,6436 0,0377 R₈ 0,1409 0,0082 Env₁ 0,7387 0,1593 R₉ 0,7387 0,1176 R₁₀ 0,7387 0,0244 R₁₁ 0,1081 0,0172 Env₂ 0,1532 0,0330 R₁₂ 0,5092 0,0168 R₁₃ 0,0809 0,0027 R₁₄ 0,2226 0,0074 R₁₅ 0,1872 0,0062 Env₃ 0,1081 0,0233 R₁₆ 0,2654 0,0062 R₁₇ 0,5580 0,0130 R₁₈ 0,1200 0,0028 R₁₉ 0,0566 0,0013 Soc₁ 0,7388 0,1041 R₂₀ 0,0769 0,0080 R₂₁ 0,5400 0,0562 R₂₂ 0,3831 0,0399 Soc₂ 0,2612 0,0368 R₂₃ 0,8994 0,0331 R₂₄ 0,1006 0,0037 = 2,06 CR= 0.00 = CR= =3.099 CR=0,09 =3.061 CR=0.053 = 4.21 CR= =4.21 CR=0.078 =3.061 CR=0.053 = 2,006 CR= 0.00

8 The -cuts fuzzy comparson matrces for all level were obtaned by fxng =0.5 and =0.5. Some revsons of judgment wth the DM were necessary as some matrces consstency ratos exceeded 0.1. Based on results shown n Table 1., among of all three key aspects the economc factor s the most sgnfcant one n selectng a 3PRL provder for ths case company as t has the hghest value of prorty weghts W=0.64.Furthermore, under Economc aspect, Overall costs was consdered the most mportant crteron wth the total weght of Then the 3PRLPs should mprove the prce besdes the other elements. Green level was determned as the most mportant sub-crtera under envronmental aspect wth the total weght of Consequently, the 3PRL provders would be better to acheve reverse logstcs actvtes wth mnmum envronmental mpact to surpass ther compettors. Moreover, n the socal aspect Customer satsfacton s the most sgnfcant subcrteron that has to be consdered by 3PRL provders. Then the 3PRL provders are evaluated regardng each sub-crteron n order to construct the evaluaton table. By usng the PROMETHEE applcaton software Vsual PROMETHEE 1.4, the outrankng flows of 3PRL provders were obtaned, as well as a sutable provder selected. The alternatve 3PRLP₁ and 3PRLP₄ are ncomparable as per PROMETHEE I but 3PRLP₁ s superor to 3PRLP₄ accordng to PROMETHEE II that gves the rankng n the preference order of RLP₃ RLP₂ RLP₁ RLP₄ as shown n Fg.2. PROMETHEE I Partal Rankng PROMETHEE II Complete Rankng Fg. 2. PROMETHEE Rankng The proposed approach has an advantage regardng the tme and the effort of selecton process compared to the exstng ones. 4 Conclusons Ths paper proposes a new ntegrated sustanable approach for selectng 3PRLP usng AHP and PROMETHEE methods under fuzzy envronment. The proposed model provdes gudelnes to the DM and the results obtaned can help not only to select the best sustanable 3PRLP, but also to understand the current state of practce n 3PRLP selecton process n the company. In our future research, ths wll be followed by

9 GAIA plane whch provdes a descrptve complement vson. Senstvty analyss of If-what scenaros wll be also carred to analyze the mpact of changng the crtera weghts on alternatves rankng. Furthermore, the proposed approach can be llustrated by other case study usng other MCDM methods lke TOPSIS, VIKOR or hybrd methods and the obtaned results can be compared wth each other n future research. References 1. Ropel,., Chounard, Marc, al,. Ing nere et geston de la logstque nverse vers des r seau durables. Hermes scence publcatons Lavoser, Pars (2011). 2. Kannan, G., Pokharel, S., Sas Kumar, P.: A hybrd approach usng ISM and fuzzy TOPSIS for the selecton of reverse logstcs provder. Resour. Conserv. Recycl. 54, (2009). 3. Nkolaou, I.E., Evangelnos, K.I., Allan, S.: A reverse logstcs socal responsblty evaluaton framework based on the trple bottom lne approach. J. Clean. Prod. 56, (2013). 4. Baumann, E. Modèles d valuaton des performances conomque, envronnementale et socale dans les chaînes logstques, (2011). 5. Kafa, N., Han, Y., El Mhamed, A.: Sustanablty Performance Measurement for Green Supply Chan Management. Presented at the 6th IFAC Conference on Management and Control of Producton and Logstcs The Internatonal Federaton of Automatc Control, center for Informaton Technology Renato Archer, Fortaleza, Brazl September 11 (2013). 6. Govndan, K., Khodaverd, R., Jafaran, A.: A fuzzy mult crtera approach for measurng sustanablty performance of a suppler based on trple bottom lne approach. J. Clean. Prod. 47, (2013). 7. Wang, J., Zhu, Y.: Research on Thrd-party Reverse Logstcs Provder Selecton Based on Fuzzy Clusterng n Perspectve of Low-carbon Economy. Commun. Inf. Sc. Manag. Eng. 2, (2011). 8. Tajk, G., Azadna, A.H., Ma aram, A.B., Hassan,.A.H.. A Hybrd Fuzzy MC M Approach for Sustanable Thrd-Party Reverse Logstcs Provder Selecton. Adv. Mater. Res. 845, (2013). 9. Kafa, N., Han, Y., El Mhamed, A.: Sustanable approach for thrd-party reverse logstcs provder selecton. Presented at the Internatonal Conference on Green Supply Chan, ARRAS - FRANCE June 25 (2014). 10. Saaty, T.L.: The analytc herarchy process. McGraw- Hll Book Co, New York (1980). 11. Kwong, C.K., Ba, H.: A fuzzy AHP approach to the determnaton of mportance weghts of customer requrements n qualty functon deployment. J. Intell. Manuf. 13, (2002). 12. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, (1965). 13. Lee, A.R.: Applcaton of modfed fuzzy ahp method to analyze boltng sequence of structural jonts, (1995). 14. Brans, J.-P., Vncke, P.: A preference rankng organzaton method: the PROMETHEE method for MCDM. Manag. Sc. 31, (1985). 15. Chen, S.H., Hseh, C.H.: Representaton, rankng, dstance, and smlarty of L-R type fuzzy number and applcaton. Aust. J. Intell. Process. Syst. 6, (2000). 16. Gupta, R., Sachdeva, A., Bhardwaj, A.: Selecton of logstc servce provder usng fuzzy PROMETHEE for a cement ndustry. J. Manuf. Technol. Manag. 23, (2012).