求解多目标流水车间调度Pareto最优解的遗传强化算法  

Genetic Reinforcement Algorithm for Solving Pareto Optimal Solutions for Multi-objective Flow Shop Scheduling

作　　者：刘宇 陈永灿 周艳平[1] LIU Yu;CHEN Yong-Can;ZHOU Yan-Ping(School of Information Science&Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2024年第2期239-245,共7页Computer Systems & Applications

摘　　要：针对多目标流水车间调度Pareto最优问题,本文建立了以最大完工时间和最大拖延时间为优化目标的多目标流水车间调度问题模型,并设计了一种基于Q-learning的遗传强化学习算法求解该问题的Pareto最优解.该算法引入状态变量和动作变量,通过Q-learning算法获得初始种群,以提高初始解质量.在算法进化过程中,利用Q表指导变异操作,扩大局部搜索范围.采用Pareto快速非支配排序以及拥挤度计算提高解的质量以及多样性,逐步获得Pareto最优解.通过与遗传算法、NSGA-II算法和Q-learning算法进行对比实验,验证了改进后的遗传强化算法在求解多目标流水车间调度问题Pareto最优解的有效性.Aiming at the Pareto optimal problem for multi-objective flow shop scheduling,this study builds a multi-objective flow shop scheduling problem model with maximum completion time and maximum delay time as the optimization objectives.Meanwhile,the study designs a genetic reinforcement learning algorithm based on Q-learning for the Pareto optimal solution of the problem.The algorithm introduces state variables and action variables and obtains the initial population by Q-learning algorithm to improve the initial solution quality.During the evolution of the algorithm,the Q-table is applied to guide the mutation operation to expand the local search range.The Pareto fast non-dominated sorting and congestion calculation are adopted to improve the solution quality and diversity,and the Pareto optimal solution is obtained step by step.The effectiveness of the improved genetic enhancement algorithm for the Pareto optimal solution of the multi-objective flow shop scheduling problem is verified by comparing the proposed algorithm with the genetic algorithm,NSGA-II algorithm,and Q-learning algorithm.

关 键 词：多目标流水车间调度 Q-LEARNING 遗传算法 PARETO 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TB497[自动化与计算机技术—控制科学与工程]