DOI:

TP278

Coordinated Two-machine Flow-Shop and Transportation Scheduling under Size Constraint and Game Theory Consideration
Author:
Affiliation:

Shenyang Ligong University

Fund Project:

Liaoning Province “Xingliao Talents Plan” project;Project supported by the Research and Innovation Team Construction Plan of Shenyang Ligong University;Scientific Research Funds Project of Educational Department of Liaoning Province(LG202025)

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摘要:

本文研究了二机流水车间生产运输协调调度问题, 当工件在第一台机器加工完成后, 由一台带有容量限制的运输车分批次运输到第二台机器加工, 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.

Abstract:

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 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.

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##### 历史
• 收稿日期:2021-12-21
• 最后修改日期:2022-03-10
• 录用日期:2022-03-15
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