The simultaneous scheduling of cranes and automated guided vehicles (AGVs) has been a critical problem, especially in traditional heavy-duty manufacturing enterprises. Previous studies mainly focus on the coscheduling problem for the container terminals rather than workshop. Hence, this study evaluated the coscheduling problem of automated single-beam cranes and AGVs incurred by an actual manufacturing environment. A joint scheduling model was established by considering the constraints of conflict-free, noncrossing, and safety distance as well as the feature of the material. This model considers the process and minimum makespan as the scheduling unit and optimization objective, respectively, including handling time and process time. Subsequently, a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) was developed, and a check-repair procedure was designed to guarantee the feasibility of optimal solution. Finally, numerical illustrative experiments and nonparametric tests were conducted to verify the effectiveness of the proposed model and algorithm, and the results show that the proposed algorithm has better performance than PSO and GA in solving the proposed model.

起重机和自动导引车（AGV）的同步调度一直是一个关键问题，尤其是在传统的重型制造企业中。先前的研究主要集中在集装箱码头的调度问题上，而不是车间。因此，本研究评估了由实际制造环境引起的自动单梁起重机和AGV的调度问题。考虑到无冲突，不交叉，安全距离以及材料特性的约束，建立了联合调度模型。该模型将过程和最小制造时间分别视为调度单位和优化目标，包括处理时间和过程时间。随后，开发了一种混合遗传算法和粒子群优化算法（HGA-PSO），设计了检修程序以保证最佳解决方案的可行性。最后，通过数值实验和非参数测试验证了所提模型和算法的有效性，结果表明所提算法在求解所提模型方面具有优于PSO和GA的性能。