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), an advanced variant of the online MAPD problem incorporating delivery deadlines. In the MAPD problem, agents must manage a continuous stream of delivery tasks online. Tasks are added at any time. Agents must complete their tasks while avoiding collisions with each other. MAPD-D introduces a dynamic, deadline-driven approach that incorporates task deadlines, challenging the conventional MAPD frameworks. To tackle MAPD-D, we propose a novel algorithm named deadline-aware token passing (D-TP). The D-TP algorithm calculates pickup deadlines and assigns 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 through 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 demonstrated significant reductions in task tardiness compared to existing methods. Our methods contribute to efficient operations in automated warehouses and factories with delivery deadlines.

翻译：在自动化仓库和工厂中管理送货截止期限对于维持客户满意度和确保生产流程顺畅至关重要。本研究提出了带任务截止期限的在线多智能体取货与送货问题（MAPD-D），这是在线MAPD问题的一个高级变体，引入了送货截止期限约束。在MAPD问题中，智能体必须在线处理持续到达的送货任务流。任务可随时添加，智能体需在相互避碰的前提下完成任务。MAPD-D通过引入任务截止期限，提出了一种动态的、截止期限驱动的任务处理方式，对传统MAPD框架提出了新的挑战。为应对MAPD-D问题，我们提出了一种名为截止期限感知令牌传递（D-TP）的新算法。该算法通过计算取货截止期限并进行任务分配，在执行成本与截止期限紧迫性之间实现平衡。此外，我们进一步提出了支持任务交换的D-TP方法（D-TPTS），通过任务交换策略降低任务延迟率，从而提升系统灵活性与效率。在模拟仓库环境中进行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和D-TPTS均显著降低了任务延迟率。我们的方法为具有送货截止期限要求的自动化仓库和工厂的高效运作提供了技术支持。