In scheduling problem for automatic assembly, planning of task sequence is closely related with resource allocation. However, they have been separately carried out with little interaction in previous work. In assembly planning problem, there are many feasible sequences for one mechanical product. In order to find the best assembly sequence, we have to decide the cost function for each task a priori and make decision based on summation of costs in sequence. But the cost of each task depends on the machine which executes the allocated task and it becomes difficult to estimate an exact cost of each task at planning stage. Moreover, no concurrent operation is taken into account at planning stage. Therefore, we must consider the sequence planning and the machine allocation simultaneously. In this paper, we propose a new scheduling method in which sequence planning and machine allocation are considered simultaneously. First of all, we propose a modeling method for an assembly sequence including a manufacturing environment. Secondly, we show a guideline in order to determine the estimate function in A* algorithm for assembly scheduling. Thirdly, a new search method based on combination of A* algorithm and supervisor is proposed. Fourthly, we propose a new technique which can take into consider the repetitive process in manufacturing system so as to improve the calculation time. Finally, numerical experiments of proposed scheduling algorithm are shown and effectiveness of proposed algorithm is verified.

This paper presents a new hierarchical scheduling method for a large-scale manufacturing system based on the hybrid Petri-net model, which consists of CPN (Continuous Petri Net) and TPN (Timed Petri Net). The study focuses on an automobile production system, a typical large-scale manufacturing system. At a high level, CPN is used to represent continuous flow in the production process of an entire system, and LP (Linear Programming) is applied to find the optimal flow. At a low level, TPN is used to represent the manufacturing environment of each sub-production line in a decentralized manner, and the MCT algorithm is applied to find feasible semi-optimal process sequences for each sub-production line. Our proposed scheduling method can schedule macroscopically the flow of an entire system while considering microscopically any physical constraints that arise on an actual shop floor.