The increasing demand for flexible automation has accelerated the adoption of heterogeneous automated guided vehicles (AGVs). This work investigates a new scheduling problem in a material transportation system consisting of attachable heterogeneous AGVs, including carriers and shuttles, that flexibly attach and detach for cooperative task execution. While such collaboration enhances operational efficiency, the attachment-induced synchronization renders the system highly coupled and susceptible to deadlocks. To address this, we propose a Petri net (PN)-based deadlock-free scheduling framework integrated into an adaptive large neighborhood search (ALNS) algorithm. The PN is introduced to map candidate solutions from static permutations into dynamic collaborative processes, enabling performance evaluation via state evolution and proactive deadlock prevention through structural analysis. Extensive experiments on real-world and synthetic instances demonstrate that the proposed framework significantly improves computational efficiency, with the developed ALNS outperforming the current on-site policy, exact solvers, and state-of-the-art metaheuristics. Finally, sensitivity analysis yields managerial insights for optimal fleet sizing.

翻译：柔性自动化需求的增长加速了异构自动导引车（AGVs）的应用。本研究探讨了一个由可附着式异构AGV（包括承载车与穿梭车）构成的物料运输系统中的新型调度问题，这些AGV通过灵活附着与分离实现协同任务执行。尽管此类协作提升了作业效率，但附着引发的同步机制导致系统高度耦合且易发生死锁。为解决这一问题，我们提出一种基于Petri网（PN）的无死锁调度框架，并将其集成到自适应大邻域搜索（ALNS）算法中。通过引入Petri网将候选解从静态排列映射为动态协作过程，从而基于状态演化进行性能评估，并通过结构分析实现主动死锁预防。在真实场景与合成实例上的大量实验表明，所提出的框架显著提升了计算效率，开发的ALNS算法优于现行现场策略、精确求解器及当前最先进的元启发式算法。最后，敏感性分析为最优车队规模配置提供了管理见解。