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<p>E-commerce growth over recent years has increased the number of shipments to customers and made the supply chain complicated. Satisfaction from e-commerce service can be affected by on-time order delivery and could motivate e-customers to repurchase. In this paper, we address a two-echelon vehicle routing problem (2E-VRP) with cost and satisfaction objectives based on the RFM (Recency, Frequency, Monetary) method called 2E-OVRPTW-MS. To take the problem closer to reality, we consider time-windows constraint, open-loop routing, and mobile satellite location. A mixed-integer linear programming model, as well as a clustering-based algorithm for the satellite location problem, is presented. Due to the NP-hardness of the 2E-VRP, a heuristic based on large neighborhood search (LNS) was developed and an experimental study to evaluate the proposed LNS was conducted solving sized small, medium, and large-sized instances. Computational results show that the proposed LNS can reach high-quality-robust solutions compared to the other algorithms. The LNS has found 16 out of 36 better solutions compared to the ALNS (5 out of 36) and VND + GRASP (0 out of 36) with a minimum average %gap.</p>

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A mathematical model for two-echelon open vehicle routing problem with time-windows and mobile satellites based on customer satisfaction

  • 期刊论文

  • 发表日期：

    2025年5月31日

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  • Mohammad Amin Adibi,
  • Adel Pourghader Chobar,
  • Soheil Mortazi

摘要

E-commerce growth over recent years has increased the number of shipments to customers and made the supply chain complicated. Satisfaction from e-commerce service can be affected by on-time order delivery and could motivate e-customers to repurchase. In this paper, we address a two-echelon vehicle routing problem (2E-VRP) with cost and satisfaction objectives based on the RFM (Recency, Frequency, Monetary) method called 2E-OVRPTW-MS. To take the problem closer to reality, we consider time-windows constraint, open-loop routing, and mobile satellite location. A mixed-integer linear programming model, as well as a clustering-based algorithm for the satellite location problem, is presented. Due to the NP-hardness of the 2E-VRP, a heuristic based on large neighborhood search (LNS) was developed and an experimental study to evaluate the proposed LNS was conducted solving sized small, medium, and large-sized instances. Computational results show that the proposed LNS can reach high-quality-robust solutions compared to the other algorithms. The LNS has found 16 out of 36 better solutions compared to the ALNS (5 out of 36) and VND + GRASP (0 out of 36) with a minimum average %gap.