Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem-Reference-Cited by-同舟云学术

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Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem

Published:2023-09 Issue: Volume:145 Page:110596
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Su Chupeng^ORCID,Zhang Cong^ORCID,Xia Dan,Han Baoan,Wang Chuang,Chen Gang,Xie Longhan

Publisher

Elsevier BV

Subject

Software

Reference88 articles.

1. Review of job shop scheduling research and its new perspectives under industry 4.0;Zhang;J. Intell. Manuf.,2019

2. A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems;Gao;IEEE/CAA J. Autom. Sin.,2019

3. The complexity of flowshop and jobshop scheduling;Garey;Math. Oper. Res.,1976

4. Production scheduling in the context of industry 4.0: Review and trends;Parente;Int. J. Prod. Res.,2020

5. Challenges in transforming manufacturing organisations into product-service providers;Martinez;J. Manuf. Technol. Manag.,2010

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1. A multiobjective optimizer with a K-means cluster algorithm for a distributed flexible flowshop rescheduling problem;Applied Soft Computing;2024-11

2. Graph reinforcement learning for flexible job shop scheduling under industrial demand response: A production and energy nexus perspective;Computers & Industrial Engineering;2024-07

3. Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer;Journal of Computational Design and Engineering;2024-05-01

4. A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem;Scientific Reports;2024-04-20

5. Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities;Swarm and Evolutionary Computation;2024-04

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