应用强化学习算法求解置换流水车间调度问题

Reinforcement Learning Algorithm for Permutation Flow Shop Scheduling to Minimize Makespan

面对日益增长的大规模调度问题,新型算法的开发越显重要.针对置换流水车间调度问题,提出了一种基于强化学习Q-Learning调度算法.通过引入状态变量和行为变量,将组合优化的排序问题转换成序贯决策问题,来解决置换流水车间调度问题.采用所提算法对OR-Library提供Flow-shop国际标准算例进行测试,并与已有的一些算法对比,结果表明算法的有效性.

In the face of increasing large-scale scheduling problems,the development of new algorithms becomes more and more important.A Q-Learning scheduling algorithm based on reinforcement learning is proposed for permutation flow shop scheduling problem.By introducing state variables and behavior variables,the scheduling problem of combinatorial optimization is transformed into sequential decision-making problem to solve the permutation flow shop scheduling problem.The proposed algorithm is used to test the Flow-shop international standard provided by OR-Library,and compared with some existing algorithms,the results show that the algorithm is effective.