带尺寸约束的二机流水车间生产运输协调博弈调度问题  

Coordinated two-machine flow-shop and transportation scheduling under size constraint and game theory consideration

作　　者：宫华[1] 许可[1] 孙文娟[1] GONG Hua;XU Ke;SUN Wen-juan(School of Science,Shenyang Ligong University,Shenyang 110159,China)

机构地区：[1]沈阳理工大学理学院,沈阳110159

出　　处：《控制与决策》2023年第7期1942-1950,共9页Control and Decision

基　　金：辽宁省“兴辽英才计划”项目(XLYC2006017);辽宁省教育厅科学研究经费项目(LG202025);沈阳理工大学科研创新团队建设计划项目(SYLUTD202102).

摘　　要：研究二机流水车间生产运输协调调度问题,当工件在第1台机器加工完成后,由1台带有容量限制的运输车分批次运输到第2台机器加工,运输过程考虑工件尺寸约束,目标函数为最小化最大完工时间.考虑到源于不同客户的工件对机器及运输设备的竞争,以工件为博弈方,工件在生产运输过程中等待时间为策略,各工件完工时间为收益,建立非合作博弈模型.通过将问题转化为马尔可夫决策过程,设计线性逼近值函数的Q-learning算法求解纳什均衡调度.实验结果表明Q-learning算法求得的纳什均衡调度具有较好的全局最优性,从而能够在满足客户的利益下,提高企业的生产效率,实现客户与企业的双赢.In this paper,a coordinated production and transportation scheduling problem on two-machine flow-shop is studied.After being processed on the first machine,the jobs are transported to the second machine in batches by a transporter with capacity limitation.Each job has different size requirement during transportation.The objective is to minimize the maximum makespan.Since different jobs belonged to different customers have the competition for the machines and the transporter,a non-cooperative game model is established.In this game model,the jobs are viewed as players,the waiting time of each job is viewed as the strategy,and the completion time of each job is viewed as its profit.By transforming the problem into a Markov decision process,a Q-learning algorithm with linear approximate value function is designed to solve Nash equilibrium scheduling.The experimental results show that the approximate Nash equilibrium scheduling obtained by the Q-learning algorithm has a better global optimality.The algorithm can improve the production efficiency and achieve a win-win situation for customers and enterprises in satisfying with the interests of customers.

关 键 词：生产运输协调调度 非合作博弈 Q-learning算法 纳什均衡 二机流水车间 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]