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

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Bas S. A., Ozkok B. A.

Operations Research and Decisions, vol.30, no.1, pp.25-46, 2020 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.37190/ord200102
  • Journal Name: Operations Research and Decisions
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, Business Source Elite, Business Source Premier, INSPEC, zbMATH, Directory of Open Access Journals
  • Page Numbers: pp.25-46
  • Keywords: closed-loop supply chain management, multi-objective optimization, fuzzy mixed-integer linear programming, inventory decision, NETWORK DESIGN, OPTIMIZATION, DEMAND
  • Yıldız Technical University Affiliated: Yes


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.