Integrated design of sustainable supply chain and transportation network using a fuzzy bi-level decision support system for perishable products

• Published in: Expert systems with applications Vol. 195; p. 116628
• Main Authors: Tirkolaee, Erfan Babaee, Aydin, Nadi Serhan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.06.2022; Elsevier BV
• Subjects: Bi-level Decision Support System; Decision support systems; Design optimization; Fuzzy Weighted Goal Programming; Goal programming; Integer programming; Linear programming; Mixed integer; Multiple objective analysis; Quality assessment; Quality indicators; Supply chains; Sustainability; Sustainable supply chain; Transportation network; Transportation networks; Uncertainty; Quality indicators; Bi-level Decision Support System; Transportation network; Sustainable supply chain; Fuzzy Weighted Goal Programming
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.116628

This study introduces a fuzzy bi-level Decision Support System (DSS) to optimize a sustainable multi-level multi-product Supply Chain (SC) and co-modal transportation network for perishable products distribution. To this end, two integrated multi-objective Mixed Integer Linear Programming (MILP) models are proposed to formulate the problem. On-time delivery is taken into account as the main factor that determines model performance due to perishability of products. Optimizing the design of SC network using the first level of the proposed DSS, the transportation network configuration is provided optimally in the second level considering different modes and options. In order to contribute to the literature, mainly by addressing uncertainty and perishability, a hybrid solution technique based on possibilistic linear programming and Fuzzy Weighted Goal Programming (FWGP) approach is developed to accommodate our suggested bi-level model. This technique can deal with problem uncertainty while also ensuring the sustainability of the overall system. Lp-metric method is implemented along with three well-known quality indicators to assess the performance of the proposed solution method and quality of obtained solutions. Finally, three illustrative numerical examples are provided using the CPLEX solver to showcase the applicability of the proposed methodology and discuss the complexity of the model. Results demonstrate the efficiency of the proposed methodology in finding optimal solutions compared to Lp-metric method, such that it is able to treat a problem with more than 2.2 million variables and 1.3 million constraints in 1093.08 s. •Designing a bi-level DSS to configure the supply chain and transportation networks.•Addressing the sustainable development of the problem by developing two MILP models.•Studying the perishability of in-stock products at manufacturing plants and DCs.•Applying fuzzy weighted goal programming approach to deal with multi-objectiveness.