Multi-objective evolutionary algorithm based flexible assembly job-shop rescheduling with component sharing for order insertion DOI
Jinghe Sun, Zhuo Zhang, Guohui Zhang

et al.

Computers & Operations Research, Journal Year: 2024, Volume and Issue: 169, P. 106744 - 106744

Published: June 21, 2024

Language: Английский

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem DOI
Hongtao Tang, Xiao Yu,

Wei Zhang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121723 - 121723

Published: Sept. 22, 2023

Language: Английский

Citations

55
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

28
Modeling and optimization algorithm for energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources DOI
Fei Yu, Chao Lu, Lvjiang Yin

et al.

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: 40, P. 100620 - 100620

Published: May 3, 2024

Language: Английский

Citations

22
An Inverse Reinforcement Learning Algorithm with Population Evolution Mechanism for The Multi-objective Flexible Job-shop Scheduling Problem under Time-of-use Electricity Tariffs DOI
Fuqing Zhao, Weiyuan Wang, Ningning Zhu

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112764 - 112764

Published: Jan. 1, 2025

Language: Английский

Citations

2
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

2
A Q-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources DOI

Rensheng Chen,

Bin Wu, Hua Wang

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 90, P. 101658 - 101658

Published: July 18, 2024

Language: Английский

Citations

14
A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0 DOI
Xiao Chang, Xiaoliang Jia, Jiahao Ren

et al.

International Journal of Production Research, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 29

Published: May 30, 2024

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and importance production processes. It is normally assumed that each machine typically operated by one at any time; therefore, shop-floor managers need to decide on most efficient assignments for machines workers. However, processing time variable uncertain due fluctuating environment caused unsteady operating conditions learning effect Meanwhile, they also balance workload while meeting efficiency. Thus a dual resource-constrained FJSP worker's fuzzy (F-DRCFJSP-WL) investigated simultaneously minimise makespan, total workloads maximum workload. Subsequently, reinforcement enhanced multi-objective memetic algorithm based decomposition (RL-MOMA/D) proposed solving F-DRCFJSP-WL. For RL-MOMA/D, Q-learning incorporated into perform neighbourhood search further strengthen exploitation capability algorithm. Finally, comprehensive experiments extensive test instances case study aircraft overhaul are conducted demonstrate effectiveness superiority method.

Language: Английский

Citations

11
A Pareto-optimality based black widow spider algorithm for energy efficient flexible job shop scheduling problem considering new job insertion DOI
Kashif Akram, M. Usman Maqbool Bhutta, Shahid Ikramullah Butt

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111937 - 111937

Published: July 6, 2024

Language: Английский

Citations

8
Scheduling analysis of automotive glass manufacturing systems subject to sequence-independent setup time, no-idle machines, and permissive maximum total tardiness constraint DOI
YunFang He, Yan Qiao, Naiqi Wu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108299 - 108299

Published: March 21, 2024

Language: Английский

Citations

6
Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles DOI
Meng Xu, Yi Mei, Fangfang Zhang

et al.

IEEE Transactions on Evolutionary Computation, Journal Year: 2023, Volume and Issue: 28(6), P. 1761 - 1775

Published: Nov. 21, 2023

Dynamic flexible job shop scheduling is an important but difficult combinatorial optimisation problem that has numerous real-world applications. Genetic programming been widely used to evolve heuristics solve this problem. Ensemble methods have shown promising performance in many machine learning tasks, previous attempts combine genetic with ensemble techniques are still limited and require further exploration. This paper proposes a novel method uses population consisting of both single individuals ensembles. The main contributions include: 1) developing evolves comprising ensembles, allowing breeding between them explore the search space more effectively; 2) proposing construction selection strategy form ensembles by selecting diverse complementary individuals; 3) designing new crossover mutation operators produce offspring from Experimental results demonstrate proposed outperforms existing traditional most scenarios. Further analyses find success attributed enhanced diversity extensive exploration achieved method.

Language: Английский

Citations

10