欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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。
英文摘要：
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.
同期刊论文项目
同项目期刊论文
期刊信息
位置：