Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling-Reference-Cited by-同舟云学术

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Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling

Published:2024-06 Issue: Volume:87 Page:101550
ISSN:2210-6502
Container-title:Swarm and Evolutionary Computation
language:en
Short-container-title:Swarm and Evolutionary Computation

Author:

Ding Linshan,Guan Zailin,Rauf Mudassar^ORCID,Yue Lei

Publisher

Elsevier BV

Reference57 articles.

1. A genetic algorithm for the Flexible Job-shop Scheduling Problem;Pezzella;Comput. Oper. Res.,2008

2. Review on flexible job shop scheduling;Xie;IET collab. intelligent manufac.,2019

3. Energy-aware multi-objective job shop scheduling optimization with metaheuristics in manufacturing industries: a critical survey, results, and perspectives;Para;Applied Sciences,2022

4. A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations;Monch;J Scheduling,2011

5. Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: a case study for the aerospace industry;Tian;Comput. Ind. Eng.,2023

Cited by 3 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. Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies;IET Collaborative Intelligent Manufacturing;2024-09

2. Low-Carbon Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning;Sustainability;2024-05-27

3. An Adaptive Search Algorithm for Multiplicity Dynamic Flexible Job Shop Scheduling with New Order Arrivals;Symmetry;2024-05-22

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