In this paper, a fuzzy multi-objective mixed-integer linear programming (FMOMILP) model is proposed that integrates sustainability three pillars into the Closed – loop Supply Chain (CLSC) network design problem under uncertainty. A two-phase solution approach is proposed to solve this problem. Firstly, the presented model is converted into multi-objective mixed-integer linear programming (MOMILP) by the weighted average method. Then, three widely used interactive fuzzy programming approaches are implemented to solve the model. The significance of the proposed model as well as the fuzzy programming solution approaches are validated through a computational experiment. The obtained results demonstrate the effectiveness of proposed model and solution approaches in designing sustainable CLSC network under uncertain environment.
ÇalikA., T.Paksoy, A.Yildizbaşi
and N.Y.PEHLivan, A decentralized model for allied closed-loop supply chains: Comparative analysis of interactive fuzzy programming approaches, International Journal of Fuzzy Systems19(2) (2017), 367–382.
2.
CalmonA. and GravesS.C., Inventory management in a consumer electronics closed-loop supply chain, Manufacturing & Service Operations Management19(4) (2016), 568–585.
3.
HasaniA., ZegordiS.H. and NikbakhshE., Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty, International Journal of Production Research50(16) (2012), 4649–4669.
4.
VahdaniB., JolaiF., Tavakkoli-MoghaddamR. and MousaviS.M., Two fuzzy possibilistic bi-objective zero-one programming models for outsourcing the equipment maintenance problem, Engineering Optimization44(7) (2012), 801–820.
5.
WernersB., An interactive fuzzy programming system, Fuzzy Sets and Systems23(1) (1987), 131–147.
6.
HwangC.L., YoonK.Multiple Attribute Decision Making —Methods and Applications, Springer-Verlag, Berlin-Heidelberg, 1981.
7.
KeyvanshokoohE., RyanS.M. and KabirE., Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition, European Journal of Operational Research249(1) (2016), 76–92.
8.
ÖzceylanE. and PaksoyT., Interactive fuzzy programming approaches to the strategic and tactical planning of a closed-loop supply chain under uncertainty, International Journal of Production Research52(8) (2014), 2363–2387.
9.
PourjavadE. and MayorgaR.V., Optimization of a network design problem of sustainable closed loop supply chain under uncertainty using multi-objective evolutionary algorithms, Advances in Production Engineering & management13(2) (2018), 216–228.
10.
PourjavadE. and MayorgaR.V., A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system, Journal of Intelligent Manufacturing (2017). 10.1007/s10845-017-1307-5.
11.
SelimH. and OzkarahanI., A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach, The International Journal of Advanced Manufacturing Technology36(3) (2008), 401–418.
12.
TanakaH. and AsaiK., Fuzzy linear programming with fuzzy numbers, Fuzzy Sets & Systems13 (1984), 1–10.
13.
ZimmermannH.-J., Fuzzy programming and linear programming with several objective functions, Fuzzy Sets and Systems1(1) (1978), 45–56.
14.
DevikaK., JafarianA. and NourbakhsV., Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of meta-heuristics hybridization techniques, European Journal of Operational Research235(3) (2014), 594–615.
15.
SubulanK., TasanA.S. and BaykasoğluA., Fuzzy mixed integer programming model for medium-term planning in a closed-loop supply chain with remanufacturing option, Journal of Intelligent & Fuzzy Systems23(6) (2012), 345–68.
16.
L.Zeballos, M.Gomes, A.P.Barbosa-Povoa and A.Q.Novais, Addressing theuncertain quality and quantity of returns in closed-loop supply chains, Computers and Chemical Engineering47 (2012) 237–247.
17.
El-SayedM., AfiaN. and El-KharbotlyA., A stochastic model for forward—reverse logistics network design under risk, Industrial Engineering58(3) (2010), 423–31.
18.
LuM., RanL. and ShenZ.-J.M., Reliable facility location design under uncertain correlated disruptions, Manufacturing & Service Operations Management17(4) (2015), 445–455.
19.
PishvaeeM.S., RabbaniM. and TorabiS.A., A robust optimization approach to closed-loop supply chain network design under uncertainty, Applied Mathematical Modelling35(2) (2011), 637–649.
20.
PishvaeeM.S. and TorabiS.A., A possibilistic programming approach for closed-loop supply chain network design under uncertainty, Fuzzy Sets and Systems161(20) (2010), 2668–83.
21.
PishvaeeM.S. and RazmiJ., Environmental supply chain network design using multi-objective fuzzy mathematical programming, Applied Mathematical Modelling36(8) (2012), 3433–46.
22.
RamezaniM., BashiriM. and Tavakkoli-MoghaddamR., A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level, Applied Mathematical Models37(1-2) (2013), 328–344.
23.
RamezaniM., KimiagariA.M. and KarimizB., Closed-loop supply chain network design: A financial approach, Applied Mathematical Modelling38(15-16) (2014), 4099–4119.
SakawaM. and YanoH., Interactive fuzzy satisficing method for multi-objective nonlinear programming problems with fuzzy parameters, Fuzzy Sets & Systems30 (1989), 221–238.
26.
GholamianN., MahdaviI., Tavakoli-MoghaddamR. and Mahdavi-amiriN., Comprehensive fuzzy multi-objective multi-product multi-site aggregate production planning decisions in a supply chain under uncertainty, Applied Soft Computing37 (2015), 585–607.
27.
KerachianR. and KaramouzM., A stochastic conflict resolution model for water quality management in reservoir—river systems, Advances in Water Resources30(4) (2007), 866–882.
28.
TorabiS.A. and HassiniE., An interactive possibilistic programming approach for multiple objective supply chain master planning, Fuzzy Sets and Systems159(2) (2008), 193–214.
29.
TorabiS., EbadianM. and R.Tanha,
Fuzzy hierarchical production planning (with a case study), Fuzzy Sets and Systems161(11) (2010), 1511–1529.
30.
AminS.H. and ZhangG., A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return, Applied Mathematical Modelling37(6) (2013), 4165–4176.
31.
AlumurS.A., NickelS., Saldanha-da GamaF. and V.Verter,
Multi-period re verse logistics network design, European Journal of Operational Research220(1) (2012), 67–78.
32.
LiangT.F., Distribution planning decisions using interactive fuzzy multi-objective linear programming, Fuzzy Sets and Systems157(10) (2006), 1303–1316.
33.
LiangT.F. and ChengH.W., Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains, Expert Systems with Applications36(2) (2009), 3367–3377.
34.
LiX.Q., ZhangB. and LiH., Computing efficient solutions to fuzzy multiple objective linear programming problems, Fuzzy Sets & Systems157(11) (2006), 1328–1332.
35.
LaiY.J. and HwangC.L., A new approach to some possibilistic linear programming problems, Fuzzy Sets and Systems49(2) (1992), 121–133.
36.
DaiZ. and LiZ., Design of a dynamic closed-loop supply chain network using fuzzy bi-objective linear programming approach, Journal of Industrial and Production Engineering34(5) (2017), 330–343.
