车间作业优化调度问题研究

Research on job shop scheduling problem

研究车间作业优化调度问题,可以提高产品质量和设备利用率,缩短生产周期等。利用粒子群算法求解车间作业优化调度问题时存在精度低和易早熟的不足,导致调度效率较低。为提高车间作业优化调度的效率,设计了一种模拟退火算法的混合粒子群优化算法。混合算法采用基于轮盘赌的方式对粒子进行编码,运用混沌思想对粒子群基本参数进行混沌优化,加入变异操作以提高种群的多样性。仿真结果表明,混合算法求解车间作业优化调度问题时具有搜索性能好、稳定性强的特点,并提高了调度的效率。

The effective job shop scheduling can improve the product quality and equipment utilization, shorten the production cycle and so on. Basic particle swarm optimization algorithm for job shop scheduling had shortcomings of low accuracy and easy to be premature, which resulted in poor scheduling efficiency. A hybrid particle swarm optimization algorithm based on simulated annealing algorithm is designed to improve the efficiency of job shop scheduling. Hybrid algorithm is used to encode the particles based on the roulette wheel method, the basic parameters of the particle swarm are optimized by using the chaos theory, and the mutation operation is added to improve the diversity of the population. The simulation results show that the hybrid algorithm for solving job shop scheduling problem has good search performance and strong stability ,which can improve the efficiency of scheduling.