多种群混合进化算法求解带工序跳跃的分布式异构批量流混合流水车间调度问题  

Multiple-Population Hybrid Evolutionary Algorithm for Solving Distributed Heterogeneous Lot-Streaming Hybrid Flowshop Scheduling Problem with Missing Operations

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作  者:陈三燕 王学武[1] 王烨 顾幸生[1] CHEN Sanyan;WANG Xuewu;WANG Ye;GU Xingsheng(Key Laboratory of Smart Manufacturing in Energy Chemical Process,Ministry of Education,East China University of Science and Technology,Shanghai 200237,China)

机构地区:[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

出  处:《华东理工大学学报(自然科学版)》2025年第2期228-241,共14页Journal of East China University of Science and Technology

基  金:国家自然科学基金(61973120,62076095)。

摘  要:针对以最小化最大完工时间、总流经时间、机器等待时间以及总加权提前时间和延迟时间为优化目标的带工序跳跃的分布式异构批量流混合流水车间调度问题,提出了一种多种群混合进化算法(Multi-Population Hybrid Evolutionary Algorithm,MPHEA)。首先,给出每个优化目标的计算方式,并制定合适的编码方案。其次,设计了总种群与4个子种群之间的并行协同进化策略。在对总种群执行混合交叉算子和变异操作后,依据快速非支配排序和拥挤距离更新种群,从而确保总种群的多样性和优质解的保留。同时,从总种群中提取出部分解,形成了4个子种群,每个子种群专注于优化一个特定目标。总种群与子种群并行进化的方式使得子种群在各自方向上进化时能够避免过分偏重于某一个目标,从而实现多个目标的均衡优化。考虑到工序跳跃的操作对调度问题的影响,采用了相应的工序跳跃启发式规则。最后,通过仿真实验验证了MPHEA解决该调度问题的有效性和优越性。A Multi-Population Hybrid Evolutionary Algorithm(MPHEA)was proposed for the distributed heterogeneous lot-streaming hybrid flowshop scheduling problem with missing operations,aiming to minimize the makespan,total flow time,idle time of machines,and total weighted earliness and tardiness.Firstly,the calculation method for each optimization objective was provided,and an appropriate coding scheme was developed.Then,a parallel coevolutionary strategy was designed between the total population and the four subpopulations.After performing hybrid crossover and mutation operations on the population,the population was updated based on Pareto dominance and crowding distance to ensure diversity and preservation of high-quality solutions.Furthermore,partial solutions were extracted from the population to form four subpopulations,each focusing on optimizing a specific objective.The parallel evolution of the population and subpopulations allows subpopulations to avoid overly focusing on a particular objective when evolving in their respective directions,thereby achieving balanced optimization of multiple objectives.Considering the impact of missing operations on scheduling,corresponding heuristic rules of missing operations was designed.Finally,the effectiveness of MPHEA in solving the scheduling problem was verified through 400 instances.The results of four well-known algorithms were compared and the empirical results showed that MPHEA outperformed the compared algorithms.

关 键 词:工序跳跃 分布式车间调度 批量流 异构工厂 高维多目标优化 

分 类 号:TB497[一般工业技术]

 

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