Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the problem of online multi-agent pickup and delivery with task deadlines (MAPD-D), which is an advanced variant of the online MAPD problem incorporating delivery deadlines. MAPD-D presents a dynamic deadline-driven approach that includes task deadlines, with tasks being added at any time (online), thus challenging conventional MAPD frameworks. To tackle MAPD-D, we propose a novel algorithm named deadline-aware token passing (D-TP). The D-TP algorithm is designed to calculate pickup deadlines and assign tasks while balancing execution cost and deadline proximity. Additionally, we introduce the D-TP with task swaps (D-TPTS) method to further reduce task tardiness, enhancing flexibility and efficiency via task-swapping strategies. Numerical experiments were conducted in simulated warehouse environments to showcase the effectiveness of the proposed methods. Both D-TP and D-TPTS demonstrate significant reductions in task tardiness compared to existing methods, thereby contributing to efficient operations in automated warehouses and factories with delivery deadlines.

翻译：在自动化仓库与工厂中管理配送截止期对于维护客户满意度及保障生产流畅性至关重要。本研究提出带任务截止期的在线多智能体取送货问题（MAPD-D），该问题是在线MAPD问题的高级变体，融合了配送截止期约束。MAPD-D采用动态截止期驱动方法，任务可随时在线添加，因而对传统MAPD框架构成挑战。为求解MAPD-D，我们提出名为截止期感知令牌传递（D-TP）的新算法。该算法通过平衡执行成本与截止期紧迫度，设计取货截止期并分配任务。此外，我们引入带任务交换的D-TP（D-TPTS）方法，通过任务交换策略进一步降低任务延迟，提升灵活性与效率。在模拟仓库环境中开展的数值实验验证了所提方法的有效性。与现有方法相比，D-TP与D-TPTS均显著降低任务延迟，从而为带配送截止期的自动化仓库与工厂的高效运营做出贡献。