A fuzzy approach to multi-objective mixed integer linear programming model for multi-echelon closed-loop supply chain with multi-product multi-time-period

• Autorzy: Akin Bas Sema , Ahlatcioglu Ozkok Beyza

Identyfikatory

DOI

10.37190/ord200102

• Języki publikacji: EN

Abstrakty

EN

By the green point of view, supply chain management (SCM), which contains supplier and location selection, production, distribution, and inventory decisions, is an important subject being examined in recent years by both practitioners and academicians. In this paper, the closed-loop supply chain (CLSC) network that can be mutually agreed by meeting at the level of common satisfaction of conflicting objectives is designed. We construct a multi-objective mixed-integer linear programming (MOMILP) model that allows decision-makers to more effectively manage firms’ closed-loop green supply chain (SC). An ecological perspective is brought by carrying out the recycling, remanufacturing and destruction to SCM in our proposed model. Maximize the rating of the regions in which they are located, minimize total cost and carbon footprint are considered as the objectives of the model. By constructing our model, the focus of customer satisfaction is met, as well as the production, location of facilities and order allocation are decided, and we also carry out the inventory control of warehouses. In our multi-product multi-component multi-time-period model, the solution is obtained with a fuzzy approach by using the min operator of Zimmermann. To illustrate the model, we provide a practical case study, and an optimal result containing a preferable level of satisfaction to the decision-maker is obtained.

Słowa kluczowe

EN

closed-loop supply chain management; multi-objective optimization; fuzzy mixed-integerlinear programming; inventory decision

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Operations Research and Decisions
• Rocznik: 2020
• Tom: Vol. 30, No. 1
• Strony: 25--46
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Akin Bas Sema

• Department of Mathematics, Faculty of Arts and Science, Yildiz Technical University, Istanbul, Turkey

autor

Ahlatcioglu Ozkok Beyza

• Department of Business Administration, Faculty of Economics and Administrative Sciences, Yildiz Technical University, Istanbul, Turkey

Bibliografia

• [1] ILGIN M.A., GUPTA S.M., Environmentally conscious manufacturing and product recovery (ECMPRO). A review of the state of the art, J. Environ. Manage., 2010, 91 (3), 563–591.
• [2] PISHVAEE M.S., RAZMI J., Environmental supply chain network design using multi-objective fuzzy mathematical programming, Appl. Math. Model., 2012, 36 (8), 3433–3446.
• [3] BOWERSOX D.J., LA LONDE B.J., SMYKAY E.W., Readings in physical distribution management. The logistics of marketing, Macmillan, 1969.
• [4] TSIAKIS P., SHAH N., PANTELIDES C.C., Design of multi-echelon supply chain networks under demand uncertainty, Ind. Eng. Chem. Res., 2001, 40 (16), 3585–3604.
• [5] MELO M.T., NICKEL S., SALDANHA-DA-GAMA F., Facility location and supply chain management. A review, Eur. J. Oper. Res., 2009, 196 (2), 401–412.
• [6] MOUSAVI S.M., BAHREININEJAD A., MUSA S.N., YUSOF F., A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network, J. Int. Manuf., 2017, 28 (1), 191–206.
• [7] HU T.L., SHEU J.B., HUANG K.H., A reverse logistics cost minimization model for the treatment of hazardous wastes, Transp. Res. Part E: Log. Transp. Rev., 2002, 38 (6), 457–473.
• [8] SAVASKAN R.C., BHATTACHARYA S., VAN WASSENHOVE L.N., Closed-loop supply chain models with product remanufacturing, Manage. Sci., 2004, 50 (2), 239–252.
• [9] KIM K., SONG I., KIM J., JEONG B., Supply planning model for remanufacturing system in reverse logistics environment, Comp. Ind. Eng., 2006, 51 (2), 279–287.
• [10] ABDALLAH T., DIABAT A., SIMCHI-LEVI D., Sustainable supply chain design: a closed-loop formulation and sensitivity analysis, Prod. Plan. Control, 2012, 23 (2–3), 120–133.
• [11] OZCEYLAN E., PAKSOY T., A mixed-integer programming model for a closed-loop supply-chain network, Int. J. Prod. Res., 2013, 51 (3), 718–734.
• [12] HASANOV P., JABER M.Y., TAHIROV N., Four-level closed loop supply chain with remanufacturing, Appl. Math. Model., 2019, 66, 141–155.
• [13] CHEN C.L., LEE W.C., Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices, Comp. Chem. Eng., 2004, 28 (6–7), 1131–1144.
• [14] PEIDRO D., MULA J., POLER R., VERDEGAY J.L., Fuzzy optimization for supply chain planning under supply, demand and process uncertainties, Fuzzy Sets Syst., 2009, 160 (18), 2640–2657.
• [15] OZKOK B.A., TIRYAKI F., A compensatory fuzzy approach to multi-objective linear supplier selection problem with multiple-item, Exp. Syst. Appl., 2011, 38 (9), 11363–11368.
• [16] SHI J., ZHANG G., SHA J., Optimal production planning for a multi-product closed loop system with uncertain demand and return, Comp. Oper. Res., 2011, 38 (3), 641–650.
• [17] AMIN S.H., ZHANG G., An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach, Exp. Syst. Appl., 2012, 39 (8), 6782–6791.
• [18] RAMEZANI M., KIMIAGARI A.M., KARIMI B., HEJAZI T.H., Closed-loop supply chain network design under a fuzzy environment, Knowl.-Based Syst., 2014, 59, 108–120.
• [19] JINDAL A., SANGWAN K.S., Closed loop supply chain network design and optimisation using fuzzy mixed-integer linear programming model, Int. J. Prod. Res., 2014, 52 (14), 4156–4173.
• [20] JINDAL A., SANGWAN K.S., SAXENA S., Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty, Proc. CIRP, 2015, 29, 656–661.
• [21] SOLEIMANI H., GOVINDAN K., SAGHAFI H., JAFARI H., Fuzzy multi-objective sustainable and green closed-loop supply chain network design, Comp. Ind. Eng., 2017, 109, 191–203.
• [22] CHEN Y.T., CHAN F.T., CHUNG S.H., PARK W.Y., Optimization of product refurbishment in closed--loop supply chain using multi-period model integrated with fuzzy controller under uncertainties, Rob. Comp.-Int. Manuf., 2018, 50, 1–12.
• [23] WU G.H., CHANG C.K., HSU L.M., Comparisons of interactive fuzzy programming approaches for closed-loop supply chain network design under uncertainty, Comp. Ind. Eng., 2018, 125, 500–513.
• [24] ZIMMERMANN H.J., Fuzzy programming and linear programming with several objective functions, Fuzzy Sets Syst., 1978, 1 (1), 45–55.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).