摘要
微粒群优化算法(Particle Swarm Optimization,PSO)是起源于鸟群和鱼群群体运动行为的研究,是在蚁群算法提出之后的又一种新的进化计算技术,具有典型的群体智能特性。本文构建了干扰为工件到达的流水车间调度干扰管理模型,其经典目标函数为最大完工时间和干扰目标函数为干扰时间差相混合。本文运用微粒群优化算法求解流水线干扰管理调度问题,给出了计算实例并进行了详细分析,并对干扰管理问题和重调度问题进行了测试分析,得出了有参考意义的结果。
Particle swarm optimization（PSO） with the typical characteristic of swarm intelligence is a kind of novel evolution algorithm after ant colony algorithm,it inspired by social behavior of bird flocking or fish schooling.In addition,computation model is set up for disruption management on the flow shop scheduling with the arrival of new job,whose objective function is mix makespan with the time difference.Particle swarms optimization algorithm is adopted to test the disruption management scheduling problems for flow shop and a detailed analysis is given.The disruption management and re-scheduling issues are also tested and analyzed.The results of the reference is obtained.
出处
《工业工程与管理》
CSSCI
北大核心
2012年第4期48-53,共6页
Industrial Engineering and Management
基金
国家自然科学基金资助项目(71271138)
教育部人文社会科学规划基金项目(10YJA630187)
上海市教育委员会科研创新项目(12ZS133)
高等学校博士点基金项目(20093120110008)
关键词
微粒群算法
流水线
干扰管理
调度问题
优化
particle swarm optimization algorithm； flow-shop； disruption management； scheduling problem； optimization
