Cited by A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints

A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations DOI

Zhengpei Zhang,

Yaping Fu, Kaizhou Gao

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 196, P. 110484 - 110484

Published: Aug. 18, 2024

Language: Английский

Citations

Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV DOI

Fayong Zhang,

Rui Li, Wenyin Gong

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 189, P. 109917 - 109917

Published: Feb. 2, 2024

Language: Английский

Citations

Multi-objective fitness landscape-based estimation of distribution algorithm for distributed heterogeneous flexible job shop scheduling problem DOI

Fuqing Zhao, Mengjie Li, Ningning Zhu

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112780 - 112780

Published: Jan. 1, 2025

Language: Английский

Citations

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

Linshan Ding, Zailin Guan, Mudassar Rauf

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 87, P. 101550 - 101550

Published: April 1, 2024

Language: Английский

Citations

Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems DOI

Yaping Fu, Yifeng Wang, Kaizhou Gao

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 120, P. 109780 - 109780

Published: Oct. 18, 2024

Language: Английский

Citations

Evolutionary computation and reinforcement learning integrated algorithm for distributed heterogeneous flowshop scheduling DOI

Rui Li, Ling Wang, Wenyin Gong

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108775 - 108775

Published: June 12, 2024

Language: Английский

Citations

Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm DOI

L. Hao, Zhiyuan Zou, Xu Liang

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110829 - 110829

Published: Jan. 1, 2025

Language: Английский

Citations

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions DOI

Maziyar Khadivi,

Todd Charter, Marjan Yaghoubi

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110856 - 110856

Published: Jan. 1, 2025

Language: Английский

Citations

Co-Evolutionary NSGA-III with deep reinforcement learning for multi-objective distributed flexible job shop scheduling DOI

Yingjie Hou,

Xiaojuan Liao,

Guangzhu Chen

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110990 - 110990

Published: Feb. 1, 2025

Language: Английский

Citations

Double DQN-Based Coevolution for Green Distributed Heterogeneous Hybrid Flowshop Scheduling With Multiple Priorities of Jobs DOI

Rui Li, Wenyin Gong, Ling Wang

et al.

IEEE Transactions on Automation Science and Engineering, Journal Year: 2023, Volume and Issue: 21(4), P. 6550 - 6562

Published: Nov. 1, 2023

Distributed manufacturing involving heterogeneous factories presents significant challenges to enterprises. Furthermore, the need prioritize various jobs based on order urgency and customer importance further complicates scheduling process. Consequently, this study addresses practical issue by tackling distributed hybrid flow shop problem with multiple priorities of (DHHFSP-MPJ). The primary objective is simultaneously minimize total weighted tardiness energy consumption. To solve DHHFSP-MPJ, a double deep Q-network-based co-evolution (D2QCE) developed four features: i) global local searches are allocated into two populations balance computational resources; ii) A heuristic strategy proposed obtain an initialized population great convergence diversity; iii) Four knowledge-based neighborhood structures accelerate converging. Next, Q-Network applied learn operator selection; iv) An energy-efficient presented save energy. verify effectiveness D2QCE, five state-of-the-art algorithms compared 20 instances real-world case. results numerical experiments indicate that: D2QN can fast only consuming few computation resources select best operator. Combining vastly improve performance evolutionary for solving scheduling. D2QCE has better than state-of-the-arts DHHFSP-MPJ Note Practitioners —This paper inspired encountered in blanking workshop systems within large engineering equipment. In scenario, come varying distinct due dates. Balancing these priority date constraints while efficiently considerable volume enhance enterprise profitability poses challenge. Thus, abstracted jobs. objectives minimizing delay Notably, model never been studied before. address this, we've formulated mixed-integer linear programming novel co-evolutionary algorithm Q-networks (DQN). Our approach introduces several key components. First, we present framework strike between search aspects. Additionally, devised three problem-specific enhancement strategies expedite convergence, which include initialization, techniques, energy-saving measures. learning process selecting optimal minimal resources, employ DQN. Experimental demonstrate superior our approach, outperforming when summary, work proposes extended DHHFSP provides case designing learning-assisted algorithm. However, online reinforcement (DRL) consumes additional time, generalization DRL needs be improved. future research, will consider dynamic events such as new insert change workshop. Moreover, end-to-end considered realize sustainable DRL.

Language: Английский

Citations