Cited by A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning DOI

Jingrui Wang, Yi Li, Zhongwei Zhang

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(11), P. 2423 - 2423

Published: Nov. 2, 2024

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, impact dynamic disturbance events on production, need for energy-efficient integrated scheduling production equipment automated guided vehicles (AGVs) in a flexible job shop environment is investigated this study. Firstly, static model AGVs (ISPEA) developed based mixed-integer programming, which aims to optimize maximum completion time total energy consumption (EC). In recent years, reinforcement learning, including deep learning (DRL), has demonstrated significant advantages handling workshop issues with sequential decision-making characteristics, can fully utilize vast quantity historical data accumulated adjust plans timely manner changes conditions demand. Accordingly, DRL-based approach introduced address common disturbances emergency order insertions. Combined characteristics ISPEA problem an event-driven strategy events, four types agents, namely workpiece selection, machine AGV target selection are set up, refine status as observation inputs generate rules selecting workpieces, machines, AGVs, targets. These agents trained offline using QMIX multi-agent framework, utilized solve problem. Finally, effectiveness proposed method validated through comparison solution performance other typical optimization algorithms various cases.

Language: Английский

Citations

Multi-agent deep reinforcement learning-based approach for dynamic flexible assembly job shop scheduling with uncertain processing and transport times DOI

Hao Wang,

W. Lin,

Tao Peng

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126441 - 126441

Published: Jan. 1, 2025

Language: Английский

Citations

Dynamic scheduling for multi-objective flexible job shop via deep reinforcement learning DOI

Erdong Yuan, Liejun Wang, Shiji Song

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: 171, P. 112787 - 112787

Published: Jan. 25, 2025

Language: Английский

Citations

A novel hybrid intelligent scheduling: integrating human feedback into reinforcement learning for adaptive preference objectives DOI

Chen Ding, Fei Qiao, Dongyuan Wang

et al.

International Journal of Production Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Feb. 19, 2025

Language: Английский

Citations

Discrete Multi-Objective Grey Wolf Algorithm Applied to Dynamic Distributed Flexible Job Shop Scheduling Problem with Variable Processing Times DOI

J. S. Chen,

Chun Wang, Binzi Xu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2281 - 2281

Published: Feb. 20, 2025

Uncertainty in processing times is a key issue distributed production; it severely affects scheduling accuracy. In this study, we investigate dynamic flexible job shop problem with variable (DDFJSP-VPT), which the time follows normal distribution. First, mathematical model established by simultaneously considering makespan, tardiness, and total factory load. Second, chance-constrained approach employed to predict uncertain generate robust initial schedule. Then, heuristic method involves left-shift strategy, an insertion-based local adjustment DMOGWO-based global rescheduling strategy developed dynamically adjust plan response context of uncertainty. Moreover, hybrid initialization scheme, discrete crossover, mutation operations are designed high-quality population update wolf pack, enabling GWO effectively solve problem. Based on parameter sensitivity study comparison four algorithms, algorithm’s stability effectiveness both static environments demonstrated. Finally, experimental results show that our can achieve much better performance than other rules-based reactive methods hybrid-shift strategy. The utility prediction also validated.

Language: Английский

Citations

Research on dynamic job shop scheduling problem with AGV based on DQN DOI

Zhengfeng Li,

Wanfa Gu,

Huichao Shang

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning DOI

Meng Xu, Yi Mei, Fangfang Zhang

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 15, 2025

Language: Английский

Citations

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

Xiaoting Dong, Guangxi Wan, Peng Zeng

et al.

Complex & Intelligent Systems, Journal Year: 2025, Volume and Issue: 11(5)

Published: March 17, 2025

Language: Английский

Citations

Categorized Attention Based Hierarchical-agents Reinforcement Learning for Multi-objective Dynamic Job Shop Scheduling Problem With Machine Deterioration DOI

Yibing Li,

X. Liang,

Jun Guo

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113032 - 113032

Published: March 1, 2025

Language: Английский

Citations

Dynamic scheduling in flexible and hybrid disassembly systems with manual and automated workstations using reward-shaping enhanced reinforcement learning DOI

Jinlong Wang, Qijie Liang, Min Li

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 150, P. 110588 - 110588

Published: March 21, 2025

Language: Английский

Citations