A reverse logistics network for recovery systems and a robust metaheuristic solution approach

One of the major concerns in a reverse supply chain management (RSCM) system is dealing with returned products (due to being defective or obsolete) through a reverse logistics, such that the returned items reach their final destinations with minimum cost. In this paper, for managing returned products, a comprehensive seven-layer recovery network is designed, including primary customers, collection/redistribution centers, recovery, recycling and disposal centers, and secondary customers. The network is mathematically modeled as a mixed integer linear programming (MILP) model whose optimal solution determines the proper collection and recycling centers for the reverse and forward logistics of returned and recovered products, such that the total cost is minimized. Since the problem belongs to the network design class of problems which is NP-hard, the time for obtaining an optimal solution grows exponentially as the number of binary variables increases. Therefore, a new Tabu search-based heuristic method is developed for computing optimal or near-optimal solutions for the recovery system. Also, the Taguchi experimental design technique is employed for parameter tuning of the heuristic and coming up with a robust design. The efficiency and effectiveness of the proposed heuristic method has been evaluated through comparisons with a recently developed SA method, as well as the global optimal solutions of the model. Experimental results showed that the new Tabu search-based approach outperforms the SA method and has an average solution gap of 3.28 % with optimal solutions and a robustness of 2.18 %.

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