A Robust Multi-objective Model for Designing Reverse Supply Chain Network via Considering Dynamic Pricing under Uncertainty and a Parallel Memetic Solution Algorithm

Design of reverse supply chain network (RSCN) to efficiently manage the flow of returned products is one of the most important issues in supply chain management. Determining an acquisition price of returned products which could be affected by different factors such as product quality levels has a significant effect on strategic design of RSCN. In this paper¡ a comprehensive mathematical model for designing a multi-level RSCN with a dynamic pricing approach for returned products which is affected by product quality levels. In order to manage the uncertainty of number of returned products¡ a robust optimization based on the uncertainty budget approach is considered. The aim of the proposed model is to design the RSCN to maximize the RSCN total profit and responding to customer demands simultaneously. Due to the NP-hard nature of the network design problems¡ a memetic algorithm based on the non-dominated sorting genetic algorithm II and parallel adaptive variable neighborhood search is proposed to find the optimal Pareto solutions. The performance of the proposed memetic algorithm is compared with multiple similar algorithms. The computational results indicate a significant impact of the dynamic pricing approach on the performance of the reverse logistic network. In addition¡ using the robust optimization based on the uncertainty budget approach can efficiently handle various conservatism levels of decision makers under uncertainty of the business environment. Finally¡ the obtained results show the significant superiority of the proposed hybrid meta-heuristic algorithm to solve a multi-objective RSCN design model via considering the dynamic pricing approach under uncertainty.