A robust fuzzy stochastic multi-objective model for stone paper closed-loop supply chain design considering the flexibility of soft constraints based on Me measure

Jabbarzadeh, 2018, Closed-loop supply chain network design under disruption risks: A robust approach with real world application, Comput. Ind. Eng., 116, 178, 10.1016/j.cie.2017.12.025 Erol, 2022, Scrutinizing blockchain applicability in sustainable supply chains through an integrated fuzzy multi-criteria decision making framework, Appl. Soft Comput., 116, 10.1016/j.asoc.2021.108331 Zokaee, 2017, Robust supply chain network design: an optimization model with real world application, Ann. Oper. Res., 257, 15, 10.1007/s10479-014-1756-6 Gholizadeh, 2020, Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry, Comput. Ind. Eng., 147, 10.1016/j.cie.2020.106653 Garai, 2021, Cost-effective subsidy policy for growers and biofuels-plants in closed-loop supply chain of herbs and herbal medicines: An interactive bi-objective optimization in T-environment, Appl. Soft Comput., 100, 10.1016/j.asoc.2020.106949 Nayeri, 2020, Multi-objective fuzzy robust optimization approach to sustainable closed-loop supply chain network design, Comput. Ind. Eng., 148, 10.1016/j.cie.2020.106716 Keyvanshokooh, 2016, Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition, European J. Oper. Res., 249, 76, 10.1016/j.ejor.2015.08.028 Devika, 2014, Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques, European J. Oper. Res., 235, 594, 10.1016/j.ejor.2013.12.032 Boronoos, 2020, A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design, Environ. Dev. Sustain., 23, 3368, 10.1007/s10668-020-00723-z Fallah, 2015, Competitive closed-loop supply chain network design under uncertainty, J. Manuf. Syst., 37, 649, 10.1016/j.jmsy.2015.01.005 Chaharmahali, 2022, Modified metaheuristic algorithms to design a closed-loop supply chain network considering quantity discount and fixed-charge transportation, Expert Syst. Appl., 202, 10.1016/j.eswa.2022.117364 Hosseini Dehshiri, 2022, Multi-objective closed-loop supply chain network design: A novel robust stochastic, possibilistic, and flexible approach, Expert Syst. Appl., 206 Pishvaee, 2012, Robust possibilistic programming for socially responsible supply chain network design: A new approach, Fuzzy Sets and Systems, 206, 1, 10.1016/j.fss.2012.04.010 Asim, 2019, An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability, Sustain. Prod. Consum., 17, 298, 10.1016/j.spc.2018.11.010 Baghalian, 2013, Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case, European J. Oper. Res., 227, 199, 10.1016/j.ejor.2012.12.017 Farrokh, 2018, A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty, Fuzzy Sets and Systems, 341, 69, 10.1016/j.fss.2017.03.019 Darvishi, 2020, Integrated fabric procurement and multi-site apparel production planning with cross-docking: A hybrid fuzzy-robust stochastic programming approach, Appl. Soft Comput., 92, 10.1016/j.asoc.2020.106267 Mousazadeh, 2015, A robust possibilistic programming approach for pharmaceutical supply chain network design, Comput. Chem. Eng., 82, 115, 10.1016/j.compchemeng.2015.06.008 Pishvaee, 2010, A possibilistic programming approach for closed-loop supply chain network design under uncertainty, Fuzzy Sets and Systems, 161, 2668, 10.1016/j.fss.2010.04.010 Yu, 2019, A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles, J. Clean. Prod., 207, 772, 10.1016/j.jclepro.2018.10.006 Hosseini-Motlagh, 2020, Robust and stable flexible blood supply chain network design under motivational initiatives, Socioecon. Plann. Sci., 70, 10.1016/j.seps.2019.07.001 Mousazadeh, 2018, Accessible, stable, and equitable health service network redesign: A robust mixed possibilistic-flexible approach, Transp. Res. E Logist. Transp. Rev., 111, 113, 10.1016/j.tre.2018.01.006 Xu, 2013, Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation, Inf. Sci. (Ny)., 238, 75, 10.1016/j.ins.2013.02.011 Kazemi, 2019, A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis, Int. J. Prod. Res., 57, 4937, 10.1080/00207543.2018.1471244 Lahri, 2021, Sustainable supply chain network design problem: Using the integrated BWM, TOPSIS, possibilistic programming, and ɛ-constrained methods, Expert Syst. Appl., 168, 10.1016/j.eswa.2020.114373 Coenen, 2018, Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions, J. Clean. Prod., 201, 1, 10.1016/j.jclepro.2018.07.216 Liao, 2018, An environmental benefits and costs assessment model for remanufacturing process under quality uncertainty, J. Clean. Prod., 178, 45, 10.1016/j.jclepro.2017.12.256 Pishvaee, 2009, A stochastic optimization model for integrated forward/reverse logistics network design, J. Manuf. Syst., 28, 107, 10.1016/j.jmsy.2010.05.001 Aghababaei, 2022, A fuzzy bi-level programming approach to scarce drugs supply and ration planning problem under risk, Fuzzy Sets and Systems, 434, 48, 10.1016/j.fss.2021.02.021 Das, 2022, Designing a closed-loop supply chain for reusable packaging materials: A risk-averse two-stage stochastic programming model using CVaR, Comput. Ind. Eng., 167, 10.1016/j.cie.2022.108004 Wang, 2022, A multi-period multi-product green supply network design problem with price and greenness dependent demands under uncertainty, Appl. Soft Comput., 114, 10.1016/j.asoc.2021.108078 Talaei, 2016, A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry, J. Clean. Prod., 113, 662, 10.1016/j.jclepro.2015.10.074 Ghahremani-Nahr, 2019, A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm, Expert Syst. Appl., 116, 454, 10.1016/j.eswa.2018.09.027 Liu, 2021, A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity, Appl. Math. Model., 92, 99, 10.1016/j.apm.2020.10.042 Gilani, 2021, Optimal Design and Operation of the green pistachio supply network: A robust possibilistic programming model, J. Clean. Prod., 282, 10.1016/j.jclepro.2020.125212 Habib, 2021, A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment, J. Clean. Prod., 278, 10.1016/j.jclepro.2020.122403 Pishvaee, 2016, Novel robust fuzzy mathematical programming methods, Appl. Math. Model., 40, 407, 10.1016/j.apm.2015.04.054 Mohammed, 2017, Multi-period planning of closed-loop supply chain with carbon policies under uncertainty, Transp. Res. D Transp. Environ., 51, 146, 10.1016/j.trd.2016.10.033 Samuel, 2020, Robust closed-loop supply chain design with presorting, return quality and carbon emission considerations, J. Clean. Prod., 247, 10.1016/j.jclepro.2019.119086 Yu, 2020, A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility, J. Clean. Prod., 266, 10.1016/j.jclepro.2020.121702 Tsao, 2019, A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment, Transp. Res. E Logist. Transp. Rev., 124, 13, 10.1016/j.tre.2019.02.006 Fazli-Khalaf, 2019, Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design, Ann. Oper. Res., 283, 1079, 10.1007/s10479-017-2729-3 Salehi Sadghiani, 2015, Retail supply chain network design under operational and disruption risks, Transp. Res. E Logist. Transp. Rev., 75, 95, 10.1016/j.tre.2014.12.015 Torabi, 2016, An enhanced possibilistic programming approach for reliable closed-loop supply chain network design, Int. J. Prod. Res., 54, 1358, 10.1080/00207543.2015.1070215 Dehghan, 2018, Hybrid robust, stochastic and possibilistic programming for closed-loop supply chain network design, Comput. Ind. Eng., 123, 220, 10.1016/j.cie.2018.06.030 Atabaki, 2020, New robust optimization models for closed-loop supply chain of durable products: Towards a circular economy, Comput. Ind. Eng., 146, 10.1016/j.cie.2020.106520 Yager, 1981, A procedure for ordering fuzzy subsets of the unit interval, Inf. Sci. (Ny)., 24, 143, 10.1016/0020-0255(81)90017-7 Zadeh, 1978, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1, 3, 10.1016/0165-0114(78)90029-5 Dubois, 1987, The mean value of a fuzzy number, Fuzzy Sets and Systems, 24, 279, 10.1016/0165-0114(87)90028-5 Torabi, 2008, An interactive possibilistic programming approach for multiple objective supply chain master planning, Fuzzy Sets and Systems, 159, 193, 10.1016/j.fss.2007.08.010 Inuiguchi, 2000, Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem, Fuzzy Sets and Systems, 111, 3, 10.1016/S0165-0114(98)00449-7 Mousazadeh, 2015, A robust possibilistic programming approach for pharmaceutical supply chain network design, Comput. Chem. Eng., 82, 115, 10.1016/j.compchemeng.2015.06.008 Günay, 2020, A multi-objective robust possibilistic programming approach to sustainable public transportation network design, Fuzzy Sets and Systems Carlsson, 2001, On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems, 122, 315, 10.1016/S0165-0114(00)00043-9 Zhang, 2014, Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints, Fuzzy Sets and Systems, 255, 74, 10.1016/j.fss.2014.07.018 Mousazadeh, 2018, Health service network design: a robust possibilistic approach, Int. Trans. Oper. Res., 25, 337, 10.1111/itor.12417 Ren, 2018, Solving the general fuzzy random bilevel programming problem through memeasure-based approach, IEEE Access, 6, 25610, 10.1109/ACCESS.2018.2828706 Yu, 2000, Robust optimization model for stochastic logistic problems, Int. J. Prod. Econ., 64, 385, 10.1016/S0925-5273(99)00074-2 Tsao, 2020, A multi-objective fuzzy robust optimization approach for designing sustainable and reliable power systems under uncertainty, Appl. Soft Comput., 92, 10.1016/j.asoc.2020.106317 Ghaderi, 2018, A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design, J. Clean. Prod., 179, 368, 10.1016/j.jclepro.2017.12.218 Rezaei, 2015, Best-worst multi-criteria decision-making method, Omega (UK), 53, 49, 10.1016/j.omega.2014.11.009 Hosseini Dehshiri, 2022, A novel group BWM approach to evaluate the implementation criteria of blockchain technology in the automotive industry supply chain, Expert Syst. Appl., 198 Rezaei, 2016, Best-worst multi-criteria decision-making method: Some properties and a linear model, Omega (UK), 64, 126, 10.1016/j.omega.2015.12.001