Manufacturing Resource Scheduling Based on Deep Q-Network

作     者：ZHANG Yufei Zou Yuanhao ZHAO Xiaodong ZHANG Yufei;ZOU Yuanhao;ZHAO Xiaodong

作者机构：School of Electronic and Information EngineeringTongji UniversityShanghai 201804China 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2022年第27卷第6期

页      面：531-538页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the National Key Research and Development Plan(2019YFB1706401) 

主　　题：smart manufacturing job shop scheduling convolutional neural network deep Q-network 

摘      要：To optimize machine allocation and task dispatching in smart manufacturing factories, this paper proposes a manufacturing resource scheduling framework based on reinforcement learning(RL). The framework formulates the entire scheduling process as a multi-stage sequential decision problem, and further obtains the scheduling order by the combination of deep convolutional neural network(CNN) and improved deep Q-network(DQN). Specifically, with respect to the representation of the Markov decision process(MDP), the feature matrix is considered as the state space and a set of heuristic dispatching rules are denoted as the action space. In addition, the deep CNN is employed to approximate the state-action values, and the double dueling deep Qnetwork with prioritized experience replay and noisy network(D3QPN2) is adopted to determine the appropriate action according to the current state. In the experiments, compared with the traditional heuristic method, the proposed method is able to learn high-quality scheduling policy and achieve shorter makespan on the standard public datasets.