Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods

Abstract

In the current study, a green closed-loop supply chain network design for perishable products is investigated under uncertain conditions. The demands, rate of return and the quality of returned products stand as an uncertain parameter. The considered chain, based on the study of a dairy company, is a multi-period and multi-product that comprises suppliers, manufacturers, warehouses, retailers and collection centers. A mixed-integer linear programming (MILP) model is projected to minimize the cost and environmental pollutant, simultaneously. Besides, an innovative MILP robust model is developed for the problem under uncertainty. Due to the NP-hard nature of the problem, the research has developed an efficient heuristic, named YAG, to solve large-sized problems. Computational experiments conducted indicating that the YAG method has an average gap of less than 1.65 percent from the optimal solution within a reasonable time. Also, the YAG method finds the optimal solution in more than 34 percent of instances. The performance of the robust approach and the heuristic method is examined in a real case study and a diverse range of problems. The results revealed that the robust model compared to the deterministic model has better quality and seem quite more reliable. The effect of the product’s lifetime, bi-objective modeling and environmental pollutant are considered throughout the study. The results indicate that the effects of products’ lifetime and level of uncertainty vary for cost and environmental pollution objectives.