An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems DOI Creative Commons
Junjie Zhang, Zhipeng Lü, Junwen Ding

и другие.

Engineering, Год журнала: 2024, Номер unknown

Опубликована: Авг. 1, 2024

Язык: Английский

A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem DOI
Xixing Li, Ao Guo, Xiyan Yin

и другие.

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 794 - 823

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

0
Total slack transmission graph-based robust scheduling for flexible job shop scheduling under machine breakdowns DOI
Lingling Lv, Woo-Jin Song, Weiming Shen

и другие.

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 963 - 975

Опубликована: Май 4, 2025

Язык: Английский

Процитировано

0
Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer DOI Open Access
Hongliang Zhang, Yi Chen, Yuteng Zhang

и другие.

Computer Modeling in Engineering & Sciences, Год журнала: 2024, Номер 140(2), С. 1459 - 1483

Опубликована: Янв. 1, 2024

The distributed flexible job shop scheduling problem (DFJSP) has attracted great attention with the growth of global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor production, effective utilization resources can increase productivity.Meanwhile, energy consumption is a growing concern due to increasingly serious environmental issues.Therefore, dual resource (DFJSP-DRC) for minimizing makespan total studied in this paper.To solve problem, we present multi-objective mathematical model DFJSP-DRC propose Q-learning-based grey wolf optimizer (Q-MOGWO).In Q-MOGWO, high-quality initial solutions are generated by hybrid initialization strategy, an improved active decoding strategy designed obtain schemes.To further enhance local search capability expand solution space, two predation strategies three factory neighborhood structures based on Q-learning proposed.These enable Q-MOGWO explore space more efficiently thus find better Pareto solutions.The effectiveness addressing verified through comparison four algorithms using 45 instances.The results reveal that outperforms terms quality.

Язык: Английский

Процитировано

3
A knowledge-driven memetic algorithm for the energy-efficient distributed homogeneous flow shop scheduling problem DOI
Yunbao Xu, Xiangqian Jiang, Jun Li

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 89, С. 101625 - 101625

Опубликована: Июнь 19, 2024

Язык: Английский

Процитировано

3
An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems DOI Creative Commons
Junjie Zhang, Zhipeng Lü, Junwen Ding

и другие.

Engineering, Год журнала: 2024, Номер unknown

Опубликована: Авг. 1, 2024

Язык: Английский

Процитировано

3