Dynamic container slot allocation with empty container repositioning under stochastic demand

Jinpeng Liang, Zhongyuan Ma, Shuang Wang, Haitao Liu and Zhijia Tan

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 187, issue C

Abstract: Container slot allocation represents a critical operational decision-making challenge within the liner shipping industry, which necessitates making decisions on the transportation of loaded containers and the repositioning of empty ones under stochastic demand. We study novel dynamic allocation policy that leverage sequentially-revealed demand information to determine the slot allocation decision for both loaded and empty containers at each stage. In this paper, we develop a stochastic dynamic programming (DP) model to optimize the slot allocation decision for maximizing the expected total revenue over the planning horizon. To solve this model, we design an efficient allocation policy that makes slot allocations at each stage based on current empty container stocks, realized demand, and the mean demand at future stages. Comprehensive numerical experiments on both synthetic and realistic data demonstrate substantial revenue improvement of our approach over the commonly-used benchmark policies in practice and literature.

Keywords: Container slot allocation; Empty container repositioning; Stochastic demand; Stochastic dynamic programming; Approximate dynamic programming (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2024.103603

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