School of Electro-Mechanical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
Sensors (Basel). 2023 Jan 28;23(3):1447. doi: 10.3390/s23031447.
Petri nets (PNs) are widely used to model flexible manufacturing systems (FMSs). This paper deals with the performance optimization of FMSs modeled by Petri nets that aim to maximize the system's performance under a given budget by optimizing both quantities and types of resources, such as sensors and devices. Such an optimization problem is challenging since it is nonlinear; hence, a globally optimal solution is hard to achieve. Here, we developed a genetic algorithm combined with mixed-integer linear programming (MILP) to solve the problem. In this approach, a set of candidate resource allocation strategies, i.e., the choices of the number of resources, are first generated by using MILP. Then, the choices of the type and the cycle time of the resources are evaluated by MILP; the promising ones are used to spawn the next generation of candidate strategies. The effectiveness and efficiency of the developed methodology are illustrated by simulation studies.
Petri 网（PNs）广泛用于对柔性制造系统（FMS）建模。本文涉及通过 Petri 网对 FMS 进行的性能优化，旨在通过优化传感器和设备等资源的数量和类型，在给定预算的情况下最大化系统的性能。由于该优化问题是非线性的，因此很难获得全局最优解。为此，我们开发了一种遗传算法与混合整数线性规划（MILP）相结合的方法来解决该问题。在这种方法中，首先使用 MILP 生成一组候选资源分配策略，即资源数量的选择。然后，通过 MILP 评估资源的类型和周期时间的选择；选择有前途的资源用于生成下一代候选策略。通过仿真研究说明了所开发方法的有效性和效率。
