摘要
柔性作业车间调度问题是经典作业车间调度问题的深化,为解决实际生产系统中作业车间调度资源受限问题提供了方案.从生产能力约束条件出发构建柔性作业车间调度模型,以最大完工时间最小和最大机器负荷最小为目标函数,并提出了基于此的改进遗传算法.该算法采用基于工序和基于机器相结合的编码机制,利用改进多父代交叉算子和多点变异进行遗传操作,在充分保留父代优良基因的同时保证了种群的多样性,克服了传统遗传算法易于早熟或收敛慢的缺点.最后,通过仿真和比较实验,验证了该算法优化生产能力约束条件下柔性车间调度问题的可行性和有效性.
Flexible job-shop scheduling problem （FJSP） under the condition of production capacity constraint is the deepening of classic JSP, and it provides the specific measures to solve the problem of resources limiting of job-shop scheduling in practical production system. FJSP model is established under the condition of production capacity constraint, takes minimizing maximum finishing time and minimizing maximum machine burden as objective function, and proposes improved genetic Mgorithm （IGA） based on that. IGA applies the coding mechanism combining with operation-based coding and machine-based mechanism, uses improved multi-previous generation crossover operators and multi-point preservative crossover to conduct genetic operation, and overcomes the shortcoming of early mature and slow constringency of classic genetic algorithm with retaining excellent previous generation at the same time. Finally, this paper uses emulation and comparison experiment to verify the feasibility and effectiveness of this algorithm in optimizing FJSP under the condition of production capacity constraint.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2011年第3期505-511,共7页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(70672086)
教育部博士点基金(20070217068)
黑龙江省博士后基金(LRB06-390)
关键词
改进遗传算法
生产能力约束
柔性作业车间调度
improved genetic algorithm
production capacity constraint
flexible job-shop scheduling
