基于多策略融合量子粒子群算法的MOFFJSP研究  

Research on MOFFJSP Based on Multi-strategy Fusion Quantum Particle Swarm Optimization

作　　者：蔡敏 王艳 纪志成 Cai Min;Wang Yan;Ji Zhicheng(Engineering Research Center for Internet of Things Technology Application Ministry of Education,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学物联网技术应用教育部工程研究中心,江苏无锡214122

出　　处：《系统仿真学报》2021年第11期2615-2626,共12页Journal of System Simulation

基　　金：国家自然科学基金(61973138);国家重点研发计划(2018YFB1701903)。

摘　　要：为提高优化调度解集质量,针对多目标模糊柔性作业车间调度问题,以模糊最大完工时间、模糊机器总负载、模糊瓶颈机器负载为优化目标,提出多策略融合的量子粒子群算法。使用混沌映射提高初始种群质量,并引入莱维飞行策略增强算法跳出局部最优能力,设计一种基于机器变异的邻域搜索策略进行局部搜索。利用交叉维护精英个体的多样性,结合模拟退火进行深度寻优。考虑模糊生产成本,引入多指标加权灰靶决策模型解决调度方案决策难题。通过仿真实验验证了算法和决策模型的优越性和有效性。To improve the quality of the optimal scheduling solution set,a quantum particle swarm algorithm with multi-strategy fusion is proposed for the multi-objective fuzzy flexible job shop scheduling problem with fuzzy maximum completion time,fuzzy total machine load,and fuzzy bottleneck machine load as optimization objectives.Chaotic mapping is used to improve the initial population quality,and a Lévy flight strategy is introduced to enhance the algorithm’s ability to jump out of the local optimum.The neighborhood search strategy based on machine mutation is designed for local search.Cross operation is used to maintain the diversity of elite individuals,and simulated annealing is combined for the deep optimization search.Considering fuzzy production cost and introducing a multi-indicator weighted grey target decision model to solve the scheduling scheme decision problem.Simulation experiments verify the superiority and effectiveness of the algorithm and decision model.

关 键 词：模糊调度 量子粒子群算法 莱维飞行 邻域搜索 模拟退火 灰靶决策模型 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]