文章基本信息

标题：A Deep Reinforcement Learning approach for the throughput control of a Flow-Shop production system
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作者：Maria Grazia Marchesano ; Guido Guizzi ; Liberatina Carmela Santillo 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：1
页码：61-66
DOI：10.1016/j.ifacol.2021.08.006
语种：English
出版社：Elsevier
摘要：AbstractThis paper proposes a new method for controlling a flow shop in terms of throughput and Work In Process (WIP). In order to achieve a throughput target, a Deep Q-Network (DQN) is used to define the constant WIP quantity in the system. The main contribution of this paper is the novel approach used to formulate the state, action space, and reward function. An extensive pre-experimental campaign is conducted to determine the best network structure and appropriate hyperparameter values. Finally, the system’s performance is compared to the known results of an analytical model from the literature (Practical Worst Case, PWC).
关键词：KeywordsNeural networks in process controlDQNReinforcement learningFlow ShopIndustry 4.0