摘要
研究批量生产中以生产周期、最大提前/最大拖后时间、生产成本以及设备利用率指标(机床总负荷和机床最大负荷)为调度目标的柔性作业车间优化调度问题。提出批量生产优化调度策略,建立多目标优化调度模型,结合多种群粒子群搜索与遗传算法的优点提出具有倾向性粒子群搜索的多种群混合算法,以提高搜索效率和搜索质量。仿真结果表明,该模型及算法较目前国内外现有方法更为有效和合理。最后,从现实生产实际出发给出多目标批量生产柔性调度算例,结果可行,可对生产实践起到一定的指导作用。
The problem of multi-objective flexible job shop scheduling optimization of batch production is studied, where multi-objects of makespan, earliness/tardiness, production cost and equipment utilization rate （total and maximum machine tool loads） are concerned. The strategy of job shop scheduling optimization of batch production is proposed. The model of multibjective scheduling optimization is set up. Aiming at improving searching efficiency and searching quality, multiple population hybrid algorithm combining both advantages of particle swarm optimization and genetic algorithm is presented. A simulation experiment is carried out to illustrate that the proposed model and algorithm is more efficiency and feasible than that used in home and abroad in existence at present. Finally, from the fact of production, a example of multi-objective flexible job shop scheduling optimization in batch production is addressed. The experimental results can play a definite part in directing production.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2007年第8期148-154,共7页
Journal of Mechanical Engineering
基金
国家自然科学基金(59990470)。
关键词
柔性车间调度
多目标优化
进化算法
批量生产
Flexible job shop scheduling Multi-objective optimization Evolutionary algorithm Batch production
