文献类型：期刊文献

英文题名：A NOVEL SOLUTION TO JSPS BASED ON LONG SHORT-TERM MEMORY AND POLICY GRADIENT ALGORITHM

作者：Ren, J. F.[1,2];Ye, C. M.[1];Yang, F.[3]

第一作者：任剑锋;Ren, J. F.

通讯作者：Ye, CM[1]

机构：[1]Univ Shanghai Sci & Technol, Sch Business, Shanghai 200093, Peoples R China;[2]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450018, Peoples R China;[3]Henan Univ Chinese Med, Sch Management, Zhengzhou 450018, Peoples R China

第一机构：Univ Shanghai Sci & Technol, Sch Business, Shanghai 200093, Peoples R China

通讯机构：[1]corresponding author), Univ Shanghai Sci & Technol, Sch Business, Shanghai 200093, Peoples R China.

年份：2020

卷号：19

期号：1

起止页码：157-168

外文期刊名：INTERNATIONAL JOURNAL OF SIMULATION MODELLING

收录：;Scopus(收录号：2-s2.0-85085163822);WOS:【SCI-EXPANDED(收录号:WOS:000520410200014)】；

基金：The study was supported by National Natural Science Foundation, China (No. 71840003); Technology Development Project of University of Shanghai for Science and Technology, China (No. 2018KJFZ043).

语种：英文

外文关键词：Job-Shop Scheduling Problem (JSP); Long Short-Term Memory (LSTM); Pointer Network; Policy Gradient Algorithm

摘要：Based on long short-term memory (LSTM) and policy gradient algorithm, this paper proposes a novel solution to the job-shop scheduling problems (JSPs). Firstly, two LSTM networks with identical structures were established, serving as the encoding and decoding networks, respectively. Next, a pointer network was introduced to determine the job with the highest priority in the current state, creating a job sequence. Another neural network (NN) was constructed to evaluate the current job sequence. The evaluation results were taken as the baseline of the policy gradient algorithm for reinforcement learning. Then, the job sequence was optimized and updated by gradient descent. The effectiveness of our method was demonstrated through contrastive experiments on benchmark problems.