摘要
为降低并行机作业车间等量分批多目标优化调度问题的复杂度,提高优化效率,提出了一种基于仿真技术和改进非支配排序遗传算法的分步优化方法。建立了一类以完工时间最短和总制造成本最低为优化目标的并行机作业车间等量分批多目标优化调度模型;将各产品进行等量分批,以Witness为仿真平台建立并行机作业车间等量分批生产仿真模型,通过组合仿真优化得到产品理想的等量分批方案,从而将原问题转化为并行机作业车间多目标优化调度问题;设计了一种改进的非支配排序遗传算法,对并行机作业车间多目标优化调度进行求解。通过算例分析验证了该方法的有效性。
To reduce the complexity and improve the solution efficiency of the equal lot scheduling problem of Job Shop with parallel machines,a stepwise optimization method based on simulation technology and improved Non-dominated Sorting Genetic Ⅱ（NSGA Ⅱ） algorithm was proposed.A multi-objective optimization model for the equal lot scheduling problem of Job Shop with parallel machines was constructed with the objectives to minimize the makespan and manufacturing cost.The production task of each product was divided into sub-tasks with equal lot.Taking Witness as the simulation platform,a production simulation model based on the equal lot scheduling of Job Shop with parallel machines was established.Based on the model,the ideal batching scheme was obtained through the combinatorial simulation optimization.Thus,the original scheduling problem was transformed to a multi-objective optimization scheduling problem for Job Shop with parallel machines.An improved NAGA Ⅱ algorithm was designed to resolve the multi-objective optimization scheduling problem for Job Shop with parallel machines.The effectiveness of the scheduling method proposed was validated by case study.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第4期816-825,共10页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(71071173)
教育部"新世纪优秀人才支持计划"资助项目(NCET-07-0908)
教育部高校博士点科研基金资助项目(20090191110004)
重庆市重点科技攻关资助项目(2010GGB108)~~
关键词
作业车间调度
并行机
多目标优化
仿真技术
遗传算法
Job Shop scheduling
parallel machines
multi-objective optimization
simulation technology
genetic algorithm
