An initial manufacturing plan may not consider the particular implementation of flexible manufacturing cells (FMCs). Therefore, the FMC is subject to modelling and simulation to evaluate and correct the production plan using feedback. In the case of highly shared resource contention, deadlock and blocking present an inevitable, unavoidable problem, and corrections may be required for production plans. However, by using existing Petri net model theories and certain simulation software for model establishment, the structure and scale of the model may vary with changes in the parts, machines, and robots. This results in a cumbersome and complicated model building process. To address this practical problem, a token-oriented Petri net model theory is therefore proposed. The movable directions of a token are detected to determine if the firing conditions are satisfied. To avoid deadlock, the token first predicts the status of resource utilization prior to decision-making when entering the transporting-transition state. Accordingly, during the model run, events are triggered against the expected time, and transitions are thereby enabled and fired. An improved machine processing plan and robotic transporting plan may be obtained via path scenario deduction. In this study, classic case data were processed for simulation analysis of the manufacturing job, which verified the validity of the model algorithm.

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Group shop has the character of job shop and open shop, one robot is used to transport material, and the robotic cell produces multiple types of parts for a long time. In order to improve throughput, or minimize the cycle time, we establish the model of robotic cell, develop optimizing algorithm, and do computational experiments to find the better activity sequence for the robot.

This paper considers the cyclic scheduling problem in robotic cell of molds manufacture processing multiple piece types. The objective is minimizing the cycle time. A mathematical model for the scheduling problem is established, the sequencing of pieces and robot moves are combined them naturally in this practical area. At last, the probable algorithm and experiment are proposed.

In this paper, in order to optimize the move sequence of material transporting robot in molds assembly, a mathematic model is established, genetic algorithm is used to search, and computational experiments are proposed. At last, it can find that the recommend genetic algorithm has better effective.