Massive power batteries (PBs) are crucial to new energy vehicle enterprises. Due to Extended Producer Responsibility (EPR), the third-party reverse logistics provider selection(3PRLPs) process has become an important decision to save cost. This paper uses an innovative combination of qualitative analysis and quantitative data integration to address the PB 3PRLPs problem by using Failure Modes and Effects Analysis (FMEA) and fuzzy evidential reasoning (FER). Firstly, the possible failures and potential effects in the PB 3PRLPs are identified by the FMEA to determine criteria and importance grades. Subsequently, AHP is utilized to calculate the criteria weight based on the importance of grades. FER is creatively applied to address the intersection of assessment grades and allocate the belief degree (BD) of the interaction to fuse heterogeneous data. Additionally, sensitivity analysis is done to look into the stability of the sequencing. Compared with other methods, the proposed method not only solves the subjectivity of AHP weighting but also manipulates probabilistic and fuzzy uncertainties for multi-criteria decision-making (MCDM). This method is useful in quantitatively analyzing the 3PRLPs problem and in providing auxiliary decision support for enterprises.
IslamMTHudaN. Reverse logistics and closed-loop supply chain of waste electrical and electronic equipment (WEEE)/E-waste: a comprehensive literature review. Resour Conserv Recycl2018; 137: 48–75.
4.
PrakashCBaruaMK. An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment. Resour Conserv Recycl2016; 108: 63–81.
5.
KrumwiedeDWSheuC. A model for reverse logistics entry by third-party providers. Omega (Westport)2002; 30: 325–333.
6.
StamatisDH. Failure mode and effect analysis: FMEA from theory to execution-Amer Society for Quality. New York: ASQ Quality Press, 2003.
7.
LiYKannanDGargK, et al.Business orientation policy and process analysis evaluation for establishing third party providers of reverse logistics services. J Cleaner Prod2018; 182: 1033–1047.
8.
GovindanKPokharelSKumarPS. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resour Conserv Recycl2009; 54: 28–36.
9.
LiouJJHWangHSHsuCC,et al.A hybrid model for selection of an outsourcing provider. Appl Math Model2011; 35: 5121–5133.
10.
SenthilSSrirangacharyuluBRameshA. A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Syst Appl2014; 41: 50–58.
11.
Tavakkoli-MoghaddamRGitinavardHMousaviSM,et al. An interval-valued hesitant fuzzy TOPSIS method to determine the criteria weights In: Outlooks and Insights on Group Decision and Negotiation: 15th International Conference, GDN 2015, Warsaw, Poland, 22–26 June 2015, Proceedings 15, Springer, pp. 157–169.
12.
GitinavardHMousaviSM. A new balancing and ranking method based on hesitant fuzzy sets for solving decision-making problems under uncertainty. Int J Eng2015; 28: 214–223.
13.
BorujeniMPGitinavardH. Evaluating the sustainable mining contractor selection problems: an imprecise last aggregation preference selection index method. J Sustain Min2017; 16: 207–218.
14.
EbrahimnejadSNaeiniMAGitinavardH,et al.Selection of IT outsourcing services’ activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach. J Intell Fuzzy Syst2017; 32: 4081–4093.
15.
MousaviSMGitinavardH. An extended multi-attribute group decision approach for selection of outsourcing services activities for information technology under risks. Int J Appl Decis Sci2019; 12: 227–241.
16.
BehzadipourAGitinavardHAkbarpour ShiraziM. A novel hierarchical dynamic group decision-based fuzzy ranking approach to evaluate the green road construction suppliers. Sci Iran2022: 1–26. DOI: https://doi.org/10.24200/sci.2022.58112.5571.
17.
MaviRKGohMZarbakhshniaN. Sustainable third-party reverse logistic provider selection with fuzzy SWARA and fuzzy MOORA in plastic industry. Int J Adv Manuf Technol2017; 91: 2401–2418.
18.
WuXDengHLiH,et al.Impact of energy structure adjustment and environmental regulation on air pollution in China: simulation and measurement research by the dynamic general equilibrium model. Technol Forecast Soc Change2021; 172: 121010. DOI: https://doi.org/10.1016/j.techfore.2021.121010.
19.
TavanaMZareinejadMSantos-ArteagaFJ,et al.A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers. Int J Adv Manuf Technol2016; 86: 1705–1721.
20.
AguezzoulA. Third-party logistics selection problem: a literature review on criteria and methods. Omega-Int J Manage Sci2014; 49: 69–78.
21.
ChenLDuanDMishraAR,et al.Sustainable third-party reverse logistics provider selection to promote circular economy using new uncertain interval-valued intuitionistic fuzzy-projection model. J Enterp Inf Manag2022; 35: 955–987.
22.
YangCWangQPanM, et al.A linguistic Pythagorean hesitant fuzzy MULTIMOORA method for third-party reverse logistics provider selection of electric vehicle power battery recycling. Expert Syst Appl2022; 198: 116808.
23.
LiY-LYingC-SChinK-S, et al.Third-party reverse logistics provider selection approach based on hybrid-information MCDM and cumulative prospect theory. J Cleaner Prod2018; 195: 573–584.
24.
GovindanKAgarwalVDarbariJD,et al.An integrated decision making model for the selection of sustainable forward and reverse logistic providers. Ann Oper Res2019; 273: 607–650.
25.
ZarbakhshniaNSoleimaniHGhaderiH. Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Appl Soft Comput2018; 65: 307–319.
26.
MohammadkhaniAMousaviSM. A new last aggregation fuzzy compromise solution approach for evaluating sustainable third-party reverse logistics providers with an application to food industry. Expert Syst Appl2023; 216: 119396.
27.
ChunguangBSarkisJ. Integrating and extending data and decision tools for sustainable third-party reverse logistics provider selection. Comput Oper Res2019; 110: 188–207.
28.
BaliSGunasekaranAAggarwalS, et al.A strategic decision-making framework for sustainable reverse operations. J Cleaner Prod2022; 381: 135058.
29.
TavanaMZareinejadMCaprioDD,et al.An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Appl Soft Comput2016; 40: 544–557.
30.
LaiKH. Service capability and performance of logistics service providers. Trans Res E Logist Transp Rev2004; 40: 385–399.
31.
GovindanKPalaniappanMZhuQ,et al.Analysis of third party reverse logistics provider using interpretive structural modeling. Int J Prod Econ2012; 140: 204–211.
32.
YangJB. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur J Oper Res2001; 131: 31–61.
33.
YangJ-BXuD-L. On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Transactions on Systems, Man & Cybernetics: Part A, (2002).
34.
YangJBWangYMXuDL,et al.The evidential reasoning approach for MADA under both probabilistic and fuzzy uncertainties. Eur J Oper Res2006; 171: 309–343.
35.
WangYMYangJBXuDL. Environmental impact assessment using the evidential reasoning approach. Eur J Oper Res2006; 174: 1885–1913.
36.
WangY-MYangJ-BXuD-L,et al.On the centroids of fuzzy numbers. Fuzzy Sets Syst2006; 157: 919–926.
37.
PengBZhengCWeiG,et al.The cultivation mechanism of green technology innovation in manufacturing industry: From the perspective of ecological niche. J Cleaner Prod2020; 252: 119711. DOI: https://doi.org/10.1016/j.jclepro.2019.119711.
38.
GovindanKKadzinskiMEhlingR,et al.Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega-Int J Manage Sci2019; 85: 1–15.
39.
JungH. Evaluation of third party logistics providers considering social sustainability. Sustainability2017; 9: 777. DOI: https://doi.org/10.3390/su9050777.
40.
WuXLiaoH. An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Inf Fusion2018; 43: 13–26.
41.
LiaoHQinRGaoC, et al.Score-HeDLiSF: a score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Inf Fusion2019; 48: 39–54.
42.
KannanDGargKJhaPC,et al.Integrating disassembly line balancing in the planning of a reverse logistics network from the perspective of a third party provider. Ann Oper Res2017; 253: 353–376.
43.
MahmoudzadehMMansourSKarimiB. To develop a third-party reverse logistics network for end-of-life vehicles in Iran. Resour Conserv Recycl2013; 78: 1–14.
44.
PourjavadEMayorgaRV. A fuzzy rule-based approach to prioritize third-party reverse logistics based on sustainable development pillars. J Intell Fuzzy Syst2018; 35: 3125–3138.
45.
GuarnieriPSobreiroVANaganoMS,et al.The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. J Cleaner Prod2015; 96: 209–219.
46.
MishraARRaniPPandeyK. Fermatean fuzzy CRITIC-EDAS approach for the selection of sustainable third-party reverse logistics providers using improved generalized score function. J Ambient Intell Humaniz Comput2021; 96: 209–219.
47.
LiuYZhouPLiL,et al.An interactive decision-making method for third-party logistics provider selection under hybrid multi-criteria. Symmetry-Basel2020; 12: 729.
SenDKDattaSMahapatraSS. Decision support framework for selection of 3PL service providers: dominance-based approach in combination with grey set theory. Int J Inf Technol Decis Mak2017; 16: 25–57.
Abdel-BassetMGamalAElhosenyM, et al.A conceptual hybrid approach from a multicriteria perspective for sustainable third-party reverse logistics provider identification. Sustainability2021; 13: 4615. DOI: https://doi.org/10.3390/su13094615.
52.
BaidyaJGargHSahaA, et al.Selection of third party reverses logistic providers: an approach of BCF-CRITIC-MULTIMOORA using Archimedean power aggregation operators. Complex Intell Syst2021; 7: 2503–2530. DOI: https://doi.org/10.1007/s40747-021-00413-x.
53.
RostamzadehREsmaeiliASivileviciusH,et al.A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy aras. Transport2020; 35: 635–657.
54.
ZhangXSuT. The dominance degree-based heterogeneous linguistic decision-making technique for sustainable 3PRLP selection. Complexity2020; 2020: 1–18.
55.
GovindanKSarkisJPalaniappanM. An analytic network process-based multicriteria decision making model for a reverse supply chain. Int J Adv Manuf Technol2013; 68: 863–880.
56.
MishraARRaniPKrishankumarR, et al.A hesitant fuzzy combined compromise solution framework-based on discrimination measure for ranking sustainable third-party reverse logistic providers. Sustainability2021; 13: 2064. DOI: https://doi.org/10.3390/su13042064.
57.
TosarkaniBMAminSH. A multi-objective model to configure an electronic reverse logistics network and third party selection. J Cleaner Prod2018; 198: 662–682.
58.
LuoZLiZ. A MAGDM method based on possibility distribution hesitant fuzzy linguistic term set and its application. Mathematics2019; 7: 1063. DOI: https://doi.org/10.3390/math7111063.
59.
TolooMKeshavarzEHatami-MarbiniA. An interval efficiency analysis with dual-role factors. Or Spectrum2021; 43: 255–287.
60.
ShakourlooAKazemiAJavadMOM. A new model for more effective supplier selection and remanufacturing process in a closed-loop supply chain. Appl Math Model2016; 40: 9914–9931.
61.
LiuAJiXLuH,et al.The selection of 3PRLs on self-service mobile recycling machine: interval-valued pythagorean hesitant fuzzy best-worst multi-criteria group decision-making. J Cleaner Prod2019; 230: 734–750.
62.
AgrawalSSinghRKMurtazaQ. Outsourcing decisions in reverse logistics: sustainable balanced scorecard and graph theoretic approach. Resour Conserv Recycl2016; 108: 41–53.
63.
LiuAJiXXuL,et al.Research on the recycling of sharing bikes based on time dynamics series, individual regrets and group efficiency. J Cleaner Prod2019; 208: 666–687.
64.
WangC-NDangT-TNguyenN-A-T. Outsourcing reverse logistics for E-commerce retailers: a two-stage fuzzy optimization approach. Axioms2021; 10: 34. DOI: https://doi.org/10.3390/axioms10010034.
65.
GovindananKJhaPCAgarwalV,et al.Environmental management partner selection for reverse supply chain collaboration: a sustainable approach. J Environ Manag2019; 236: 784–797.
66.
LiuZWangWWangD,et al.A modified ELECTRE II method with double attitude parameters based on linguistic Z-number and its application for third-party reverse logistics provider selection. Appl Intell2022; 52: 14964–14987.
67.
EydiARastgarS. A DEA model with dual-role factors and fuzzy data for selecting third-party reverse logistics provider, case study: hospital waste collection. Ain Shams Eng J2022; 13: 101561.
68.
MohammadkhaniAMousaviSM. Assessment of third-party logistics providers by introducing a new stochastic two-phase compromise solution model with last aggregation. Comput Ind Eng2022; 170: 108324.
