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Pricing and capacity optimization for underground logistics

Author

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  • Du, Xueqin
  • Yang, Linying
  • Wang, Xiuwen
  • Zha, Min
  • Zhen, Lu

Abstract

Under the background of sustainable logistics development, this study investigates an integrated optimization problem involving freight transportation in metro-based underground logistics systems (M−ULSs), which consists of a comprehensive decision set on transport pricing, capacity arrangement, and freight flow assignment. The optimization problem is formulated as an integer programming model to maximise the profit of an M−ULS. Then, a novel learning-to-rank based column generation algorithm is proposed to efficiently solve the proposed model; more specifically, a machine learning-based ranker is designed to strategically guide the search for new columns in the column generation process. Finally, based on real cases from the Shanghai Metro network, computational experiments are conducted to verify the performance of the proposed algorithm, propose the effectiveness of elastic pricing and flexible capacity strategies, and further derive managerial insights into freight rate subsidies to support operational decisions in real M−ULSs.

Suggested Citation

  • Du, Xueqin & Yang, Linying & Wang, Xiuwen & Zha, Min & Zhen, Lu, 2025. "Pricing and capacity optimization for underground logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:transe:v:203:y:2025:i:c:s1366554525004272
    DOI: 10.1016/j.tre.2025.104386
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