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中文摘要：
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懈的可行性。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。
英文摘要：
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.
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