用强化学习自动规划集装箱船配载方案，应对需求不确定。
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty
- 端到端强化学习模型，结合可行性投影与动作掩码
- 在真实规模船体和规划周期下，性能与效率超越基线方法
- 适合航运公司优化配载决策，尤其需求波动大的场景
全球经济与环境可持续性依赖高效可靠的供应链，其中集装箱航运作为环保运输方式发挥关键作用。班轮公司通过解决配载规划问题提升运营效率。由于存在大量复杂组合特性，配载规划极具挑战，通常被分解为两个NP难子问题：主配载与舱位规划。本文提出AI2STOW，一种端到端深度强化学习模型，采用可行性投影与动作掩码，在需求不确定条件下生成满足全局目标与约束（包括配对箱区布局）的主配载计划。实验结果表明，基于反映真实船舶规模与运营规划周期的模拟实例，AI2STOW在目标性能与计算效率上均优于强化学习与随机规划的基线方法。
原文摘要 · Abstract (English)
The worldwide economy and environmental sustainability depend on eff icient and reliable supply chains, in which container shipping plays a crucial role as an environmentally friendly mode of transport. Liner shipping companies seek to improve operational efficiency by solving the stowage planning problem. Due to many complex combinatorial aspects, stowage planning is challenging and often decomposed into two NP-hard subproblems: master and slot planning. This article proposes AI2STOW, an end-to-end deep reinforcement learning model with feasibility projection and an action mask to create master plans under demand uncertainty with global objectives and constraints, including paired block stowage patterms. Our experimental results demonstrate that AI2STOW outperforms baseline methods from reinforcement learning and stochastic programming in objective performance and computational efficiency, based on simulated instances reflecting the scale of realistic vessels and operational planning horizons.
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