Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem-Reference-Cited by-同舟云学术

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Multi-guided population co-evolutionary algorithm based on multiple similarity decomposition for large-scale flexible job shop scheduling problem

Published:2024-11 Issue: Volume:166 Page:112157
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Wang Cong,Wei Lixin,Sun Hao^ORCID,Hu Ziyu

Publisher

Elsevier BV

Reference54 articles.

1. A framework of cloud-edge collaborated digital twin for flexible job shop scheduling with conflict-free routing;Gao;Robot. Comput.-Integr. Manuf.,2024

2. A reinforcement learning-artificial bee colony algorithm for flexible job-shop scheduling problem with lot streaming;Li;Appl. Soft Comput.,2023

3. Solving job shop scheduling problems via deep reinforcement learning;Yuan;Appl. Soft Comput.,2023

4. A shuffled cellular evolutionary grey wolf optimizer for flexible job shop scheduling problem with tree-structure job precedence constraints;Zhu;Appl. Soft Comput.,2022

5. Joint optimization of low-carbon product family configuration and smart production line selection;Luo;Comput. Ind. Eng.,2023

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