A multi-criteria facility location-allocation of green supply chains with perishable items

Due to the increasing environmental pollution caused by industrial and transportation systems, designing green supply chains has been of great interest to researchers. Optimal facility location increases material flow, improves customer services, and reduces costs and pollution associated with transportation. This paper investigates a multiple-criteria facility location-allocation problem for perishable items in a green supply chain. To this aim, a bi-objective optimisation model, which simultaneously optimises the total cost and the CO2 emissions, is first developed. A non-dominated sorting genetic algorithm II (NSGA-II) and the Epsilon constraint method embedded in the GAMS software are utilised to find Pareto optimal solutions to the problem. The Taguchi approach is used to tune the NSGA-II algorithm parameters to find better-quality Pareto solutions. Generating 25 problem instances and using some evaluation indices such as solution quality, solution distance, and solution diversity, the above two methods' performance is compared statistically. The results show that while there is no significant difference between the two approaches in terms of the distance measure, NSGA-II performs better than GAMS in terms of the solution quality. Meanwhile, GAMS performs better than NSGA-II in terms of solution diversity.