This paper proposes a new problem by integrating the job shop scheduling, the part feeding, and the automated storage and retrieval problems. These three problems are intertwined and the performance of each of these problems influences and is influenced by the performance of the other problems. We consider a manufacturing environment composed of a set of machines (production system) connected by a transport system and a storage/retrieval system. Jobs are retrieved from storage and delivered to a load/unload area (LU) by the automated storage retrieval system. Then they are transported to and between the machines where their operations are processed on by the transport system. Once all operations of a job are processed, the job is taken back to the LU and then returned to the storage cell. We propose a mixed-integer linear programming (MILP) model that can be solved to optimality for small-sized instances. We also propose a hybrid simulated annealing (HSA) algorithm to find good quality solutions for larger instances. The HSA incorporates a late acceptance hill-climbing algorithm and a multistart strategy to promote both intensification and exploration while decreasing computational requirements. To compute the optimality gap of the HSA solutions, we derive a very fast lower bounding procedure. Computational experiments are conducted on two sets of instances that we also propose. The computational results show the effectiveness of the MILP on small-sized instances as well as the effectiveness, efficiency, and robustness of the HSA on medium and large-sized instances. Furthermore, the computational experiments clearly shown that importance of optimizing the three problems simultaneous. Finally, the importance and relevance of including the storage/retrieval activities are empirically demonstrated as ignoring them leads to wrong and misleading results.

本文通过整合作业车间调度、零件进给以及自动化存储和检索问题提出了一个新问题。这三个问题相互交织，每个问题的表现都会影响其他问题的表现，并受其影响。我们考虑由一组机器（生产系统）组成的制造环境，这些机器（生产系统）由运输系统和存储/检索系统连接。自动存储检索系统从存储中检索作业并将其传送到装载/卸载区域 (LU)。然后它们被运送到机器之间并在机器之间运送，在这些机器上，它们的操作由运输系统处理。一旦作业的所有操作都处理完毕，作业将被带回 LU，然后返回到存储单元。我们提出了一种混合整数线性规划 (MILP) 模型，该模型可以解决小型实例的最优问题。我们还提出了一种混合模拟退火 (HSA) 算法来为更大的实例找到高质量的解决方案。HSA 结合了后期接受爬山算法和多启动策略，以促进集约化和探索，同时降低计算要求。为了计算 HSA 解决方案的最优性差距，我们推导出了一个非常快速的下界过程。计算实验是在我们也提出的两组实例上进行的。计算结果显示了 MILP 在小型实例上的有效性以及 HSA 在中型和大型实例上的有效性、效率和鲁棒性。此外，计算实验清楚地表明同时优化三个问题的重要性。最后，经验证明了包括存储/检索活动的重要性和相关性，因为忽略它们会导致错误和误导性的结果。