Abstract:
Aiming at Considering the complexity of Job-shop scheduling problem, this paper proposes Genetic Reinforcement Leaning(GRL) algorithm to solve it. Several stochastic variables are introduced to transform the Job-shop scheduling problem into sequential decision-making problem, and the model environment is built through simulation to obtain system performance indices and ensure the feasibility of solution. An algorithm integrating multi-Agent Q-Learning algorithm with Genetic Algorithm(GA) is designed to solve Job-shop scheduling problem. GRL algorithm is validated by simulation and optimization experiment results.
Key words:
Genetic Reinforcement Learning(GRL),
Job-shop scheduling,
sequential decision-making,
simulation
摘要: 针对Job-shop排序问题的复杂性,提出一种遗传强化学习算法对其求解。通过引入多个随机变量,把Job-shop排序问题转换成多阶段决策问题,通过仿真手段构建作业排序问题模型环境,求取系统性能指标并保证解的可行性。设计一个多智能体Q-Learning算法和遗传算法相结合的算法用于解决Job-shop排序问题。仿真优化实验结果验证了该算法的有效性。
关键词:
遗传强化学习,
Job-shop排序,
多阶段决策,
仿真
CLC Number:
PAN Yan-chun; ZHOU Hong. Genetic Reinforcement Learning Algorithm for Job-shop Scheduling Problem[J]. Computer Engineering, 2009, 35(16): 25-28.
潘燕春;周 泓. Job-shop排序问题的遗传强化学习算法[J]. 计算机工程, 2009, 35(16): 25-28.