In this paper, we study a dynamic container slot allocation problem (DCSAP) for a liner container shipping company that aims to make an acceptance or rejection decision to each dynamically arriving container slot booking request. To capture the dynamic arrival feature and real-time acceptance/rejection decision of the booking request, we formulate the DCSAP as a dynamic programming (DP) model with the objective of maximizing the total revenues generated by accepted container booking requests over the entire booking horizon. Due to the well-known curse of dimensionality of solving a DP model, we develop a series of models to transform the intractable DP model into a solvable approximate linear programming (ALP) model. We further propose a spatiotemporal-heterogeneity-based (STH-based) decomposition preprocessor by identifying the spatiotemporal property of the DCSAP before solving the ALP model. Extensive numerical experiments are conducted to assess the applicability of the developed research methodology.

在本文中，我们研究了班轮集装箱运输公司的动态集装箱舱位分配问题（DCSAP），旨在对每个动态到达的集装箱舱位预订请求做出接受或拒绝决定。为了捕捉预订请求的动态到达特征和实时接受/拒绝决策，我们将 DCSAP 制定为动态规划（DP）模型，其目标是在整个预订范围内最大化接受的集装箱预订请求产生的总收入。由于求解 DP 模型时存在众所周知的维数灾难，我们开发了一系列模型，将棘手的 DP 模型转换为可解的近似线性规划 (ALP) 模型。我们通过在求解 ALP 模型之前识别 DCSAP 的时空属性，进一步提出了一种基于时空异质性（STH-based）的分解预处理器。进行了大量的数值实验来评估所开发的研究方法的适用性。