用自适应算法动态分配仓库装卸货位，提升调度效率。
Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
- 结合装卸货位模式、车辆分配与调度的联合优化模型。
- 自适应算法使平均延迟和完工时间降低，优于固定模式。
- 适合物流中心调度优化场景，尤其需求多变时效果更佳。
混合服务模式的货位可通过灵活处理装货与卸货卡车提升仓库效率。然而，现有研究通常在规划前预先设定货位数量与位置。本文提出一种新模型，整合货位模式决策、卡车分配与调度，实现货位模式的动态调整。具体地，设计基于Q-learning的自适应大邻域搜索（Q-ALNS）算法：通过扰动算子调整货位模式，利用破坏-重构局部搜索求解卡车分配与调度；Q-learning根据历史表现与未来收益，采用epsilon-greedy策略自适应选择算子。大量实验与统计分析表明，该算法因高效算子组合与自适应机制，在最优性差距与帕累托前沿发现上持续优于基准算法。相比预设服务模式，本方法显著降低平均延迟与完工时间，展现出更强的动态适应能力。
原文摘要 · Abstract (English)
Mixed service mode docks enhance efficiency by flexibly handling both loading and unloading trucks in warehouses. However, existing research often predetermines the number and location of these docks prior to planning truck assignment and sequencing. This paper proposes a new model integrating dock mode decision, truck assignment, and scheduling, thus enabling adaptive dock mode arrangements. Specifically, we introduce a Q-learning-based adaptive large neighborhood search (Q-ALNS) algorithm to address the integrated problem. The algorithm adjusts dock modes via perturbation operators, while truck assignment and scheduling are solved using destroy and repair local search operators. Q-learning adaptively selects these operators based on their performance history and future gains, employing the epsilon-greedy strategy. Extensive experimental results and statistical analysis indicate that the Q-ALNS benefits from efficient operator combinations and its adaptive mechanism, consistently outperforming benchmark algorithms in terms of optimality gap and Pareto front discovery. In comparison to the predetermined service mode, our adaptive strategy results in lower average tardiness and makespan, highlighting its superior adaptability to varying demands.
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