基于改进粒子群算法的车间作业排序的优化设计

兼顾车间作业排序中的制造周期和机器利用率，建立了以最小化最大完工时间为主目标、以最大化机器利用率为从目标的优化模型。设计了引入自适应技术的惯性权重，使基本粒子群算法的学习因子可动态变化地改进粒子群算法，并用该改进后的算法对车间作业排序进行了优化设计。实例研究表明：改进后的粒子群算法在收敛速度和收敛可靠性上均优于未改进的粒子群算法，在求解车间作业排序问题的应用中具有更高的求解质量。

Design of Job Shop Scheduling Based on Improved Particle Swarm Algorithm

Aiming at the changes of manufacturing cycle and machine availability, it establishes an optimization model to minimize the maximum completion time and maximize the machine availability, designs an improvedparticle swarm optimization. This optimization includes the adaptive technology and the dynamic learning factor. The improved particle swarm optimization is used for a optimum design of job shop scheduling. The ap-plication shows that the improved particle swarm optimization is much better than the standard PSO algorithm at the convergence rate and convergence reliability, and can be get a much better solution quality on the jobshop scheduling.