改进粒子群算法在考虑运输时间下的FJSP研究  

Research on FJSP of Improved Particle Swarm Optimization Algorithm Considering Transportation Time

作　　者：陈魁 毕利 Chen Kui;Bi Li(School of Information Engineering,Ningxia University,Yinchuan 750021,China)

机构地区：[1]宁夏大学信息工程学院,宁夏银川750021

出　　处：《系统仿真学报》2021年第4期845-853,共9页Journal of System Simulation

基　　金：国家自然科学基金(61662058);西部一流大学科研创新项目(ZKZD2017005)。

摘　　要：在实际的柔性作业车间调度问题(Flexible Job-shop Scheduling Problem,FJSP)生产环境中,不仅存在工件的加工时间,而且还存在工件在机器之间的运输时间,因此考虑运输时间的柔性作业车间调度更具实际意义。提出混合离散粒子群算法求解考虑运输时间的柔性作业车间调度问题。针对粒子群算法的不稳定性和早熟问题,应用了邻域搜索算法提高其稳定性,引入竞争学习机制和随机重启算法避免算法的早熟。通过实验对比近期的同类算法,证明了混合离散粒子群算法在求解考虑运输时间FJSP时的可行性和有效性。In the actual FJSP(Flexible Job-shop Scheduling Problem) production environment, there is not only the processing time of the workpiece, but also the transport time of the workpiece between the machines, so the flexible job shop scheduling considering the transport time is more practically significant. A hybrid discrete particle swarm optimization algorithm is proposed to solve the flexible job-shop scheduling problem considering transport time. Aiming at the instability and precocity of particle swarm optimization algorithm, the neighborhood search algorithm is applied to improve its stability, and the combination of competitive learning mechanism and random restart algorithm are introduced to avoid the precocity of pso algorithm. By comparing recent similar algorithms, the feasibility and effectiveness of the hybrid discrete particle swarm optimization algorithm for FJSP with transport time is proved.

关 键 词：混合离散粒子群算法 柔性作业车间调度 邻域搜索算法 竞争学习机制 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]