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The optimization scheduling problem of flexible manufacturing systems is a complex combinatorial optimization and NP-hard issue. Using timed Petri nets as the model and aiming to minimize the maximum completion time, a novel deadlock-free optimization scheduling method for a class of flexible manufacturing systems has been established through an improved particle swarm optimization algorithm. This method first adopts a two-layer coding strategy for paths and processes, establishing a one-to-one mapping relationship between processes and particle positions. Secondly, it employs a real-time online deadlock avoidance strategy to check and repair the feasibility of particles, ensuring that the searched particles can be decoded into a deadlock-free feasible scheduling sequence. Then, two improvement strategies are designed: a particle process directional adjustment strategy and a local search strategy, to enhance the algorithm′s optimization efficiency and local search capability, ensuring the rapid acquisition of optimal or sub-optimal feasible sequences. Finally, the effectiveness of the proposed algorithm is verified through two simulation experiments. Experimental results demonstrate that, compared to other existing algorithms, the improved particle swarm optimization algorithm exhibits superior optimization capability in solving the deadlock-free optimization scheduling problem of FMSs.
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Basic Information:
DOI：10.12194/j.ntu.20230710001
China Classification Code:TP18;TH165
Citation Information:
[1]LIU Huixia,ZHANG Mingxin,School of Electrical Engineering, Nantong University.Deadlock-free scheduling based on improved particle swarm algorithm for flexible manufacturing systems[J].Journal of Nantong University (Natural Science Edition),2024,23(01):38-48.DOI:10.12194/j.ntu.20230710001.
Fund Information:
山东省自然科学基金面上项目（ZR2018MF024）; 江苏省“双创博士”项目（JSSCBS20211103）; 南通市基础科学研究项目（JC2021203）; 烟台市科技创新发展计划（2022XDRH005）
2023-07-10
2023
2023-07-18
2023
1
2023-07-26
2023-07-26
2023-07-26