A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions-Reference-Cited by-同舟云学术

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A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions

Published:2024-07 Issue: Volume:133 Page:108487
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Lu Shaojun^ORCID,Wang Yongqi,Kong Min,Wang Weizhong,Tan Weimin,Song Yingxin

Publisher

Elsevier BV

Reference53 articles.

1. Conflict identification and reconciliation in a collaborative manufacturing scheduling task;Abreu;Int. J. Inf. Technol. Decis. Making,2008

2. Greedy randomized adaptive search for dynamic flexible job-shop scheduling;Baykasoğlu;J. Manuf. Syst.,2020

3. A hybrid data-driven approach for forecasting the characteristics of production disruptions and interruptions;Bazargan-Lari;Int. J. Inf. Technol. Decis. Making,2022

4. Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival;Chang;Processes,2022

5. A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem;Chen;Comput. Ind. Eng.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A two-level evolutionary algorithm for dynamic scheduling in flexible job shop environment;Evolutionary Intelligence;2024-08-23

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