Optimizing a bi-objective multi-period fish closed-loop supply chain network design by three multi-objective meta-heuristic algorithms

Abstract

Attention to a food supply chain has increased recently due to population growth and increased demand for food. Aquaculture development is advantageous as fish is a crucial constituent of the food basket of households. This study first presents a new bi-objective and multi-period mathematical model of a fish closed-loop supply chain (CLSC). The model is addressed by utilizing the multi-objective Keshtel algorithm (MOKA), NSGA-II, and MOSA). The Taguchi method is employed to tune these meta-heuristics to attain superior performance, and the ε-constraint method is used in solving small-sized problems to validate them. The results show that the exact method cannot solve large-sized problems. The solutions are compared in terms of different performance metrics. Utilizing the ‘filtering/displaced ideal solution’ DIS method, NSGA-II and MOKA with a direct distance of 0.4228 and 0.8976 have the first and second performance ranks, respectively. Also, a case study including a trout CLSC in the north of Iran is investigated. The results and the case study show that the developed model can be applied to the proposed solution approach.