摘要
针对以最小化完工时间为目标的工艺规划与调度集成(Integration of Process Planning and Scheduling,IPPS)优化问题,提出一种将粒子群算法与遗传算法、贪婪算法相结合的混合粒子群(Hybrid Particle Swarm Optimization,HPSO)算法,首先在工艺规划阶段,通过遗传算法有效地解决了多工艺路线生成问题;然后,针对多工艺路线的车间调度问题特点,构造了此问题的粒子群求解算法,最后用示例仿真验证了该算法有效可行。
To solve the integration of process planning and scheduling （IPPS） with minimal completion time as its goal,a hybrid particle swarm optimization （HPSO） algorithm is proposed to combine particle swarm, greedy and genetic algorithm. At first, the genetic algorithm is adopted to create multiple process routes. Then based on the characteristics of job-shop scheduling problem with multiple process routes, the particle swarm algorithm is constructed. Case simulation is finally done to verify the efficiency and feasibility of the proposed algorithm.
出处
《工业工程与管理》
CSSCI
北大核心
2013年第6期20-26,共7页
Industrial Engineering and Management
基金
教育部博士学科点专项科研基金(20100006110006)
中央高校基本科研业务费专项资金资助(FRF-SD-12-011B)
关键词
工艺设计
作业调度
遗传算法
粒子群算法
process planning； job-shop scheduling； genetic algorithm； particle swarm algorithm
