Closed-loop supply chain model with third-party logistics-based return management system

초록

The rapid growth of e-commerce has led to a significant increase in return volumes, highlighting the importance of the closed-loop supply chains (CLSCs). Handling returned items has become a critical challenge, as companies face uncertainty in return quantities, product conditions, and fluctuating demand in secondary markets. In this study, we develop a two-stage stochastic programming model for CLSCs that incorporates a third-party return management system (3PL-RMS). The 3PL-RMS handles returned items through multiple recovery options, including reuse, secondary-market resale, remanufacturing by suppliers, and disposal. The model captures the complexity introduced by reverse flows and integrates uncertainties related to both returns and secondary-market demand. To solve the model efficiently, we employ the sample average approximation method and a STabilization and Root-cut based Benders Decomposition (STaR-BD) algorithm. Computational experiments show that STaR-BD reduces computation time by approximately 85% compared with traditional Benders decomposition while maintaining solution quality. A case study using realistic e-commerce data shows that integrating a third-party return management system reduces total costs by up to 32% compared with a conventional supply chain model, with savings increasing at higher return rates.

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