模拟退火遗传算法在车间作业调度中的应用

摘要

During the iterative process of standard genetic algorithm ( GA), the premature convergence of population decreases the algorithm' s searching ability. Through analyzing the reason of population premature convergence during the renewal process, by introducing the selection strategy based on simulated annealing algorithm( SA), a genetic algorithm based SA model was proposed, and its application to job-shop scheduling problem(JSP) was given. The selection strategy based on SA keeps the population diversity during the iterative process, thus overcomes the defect of premature convergence. Simulation for 30 benchmarks of JSP indicates that compared with GA algorithms, SA -GA gets better result within shorter period, thus certifies the improvement of the algorithm' s searching ability by anhealing mechanism.%研究车间作业调度系统,使资源达到优化配置.针对提高产品质量,缩短周期,传统遗传算法应用于车间作业调度过程中易出现收敛速度慢、易陷入局部最优,导致作业调度效率极低.为了提高车间作业调度的效率,提出一种模拟退火遗传算法的车间作业调度方法.在遗传算法种群更新过程引入模拟退火机制,防止早熟现象的产生,使种群在更新迭代过程中保持了多样性,加快了收敛速度,克服遗传算法过早收敛的缺陷.采用的SA-GA算法能够在最短时间找作业调度的最优解,对30个车间作业调度标准测试案例进行了仿真.仿真结果表明,使相对平均误差降低了4.6%,极大的提高了车间作业调度效率,验证了在实际生产中应用的可行和优越性.