A cooperative Q-learning-based memetic algorithm for distributed assembly heterogeneous flexible flowshop scheduling DOI
Jiawen Deng, Jihui Zhang, Shengxiang Yang

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128198 - 128198

Published: May 1, 2025

Language: Английский

Enhancing multi-objective evolutionary algorithms with machine learning for scheduling problems: recent advances and survey DOI Creative Commons
Wenqiang Zhang,

Guanwei Xiao,

Mitsuo Gen

et al.

Frontiers in Industrial Engineering, Journal Year: 2024, Volume and Issue: 2

Published: Feb. 28, 2024

Multi-objective scheduling problems in workshops are commonly encountered challenges the increasingly competitive market economy. These require a trade-off among multiple objectives such as time, energy consumption, and product quality. The importance of each optimization objective typically varies different time periods or contexts, necessitating decision-makers to devise optimal plans accordingly. In actual production, confront intricate multi-objective that demand balancing clients’ requirements corporate interests while concurrently striving reduce production cycles costs. solving various problems, evolutionary algorithms have attracted attention researchers gradually become one mainstream methods solve these problems. recent years, research combining with machine learning technology has shown great potential, opening up new prospects for improving performance methods. This article comprehensively reviews latest application progress We review techniques employed enhancing algorithms, particularly focusing on types reinforcement Different categories addressed using were also discussed, including flow-shop issues, job-shop challenges, more. Finally, we highlighted faced by field outlined future directions.

Language: Английский

Citations

6
Modelling and optimization for integrated scheduling problem considering spare parts production, batch transportation and equipment operation DOI
Huining Zhuang, Qianwang Deng, Qiang Luo

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 252, P. 124194 - 124194

Published: May 14, 2024

Language: Английский

Citations

5
A Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time DOI
Ruixue Zhang, Hui Yu, Kaizhou Gao

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 90, P. 101686 - 101686

Published: Aug. 9, 2024

Language: Английский

Citations

5
A coevolutionary algorithm using Self-organizing map approach for multimodal multi-objective optimization DOI

Zongli Liu,

Yuze Yang,

Jie Cao

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111954 - 111954

Published: July 9, 2024

Language: Английский

Citations

4
A Q-learning grey wolf optimizer for a distributed hybrid flowshop rescheduling problem with urgent job insertion DOI
Shuilin Chen, Jianguo Zheng

Journal of Applied Mathematics and Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Language: Английский

Citations

0
Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey DOI Creative Commons
Wenqiang Zhang,

Xuan Bao,

Xinchang Hao

et al.

Frontiers in Industrial Engineering, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 27, 2025

The advent of Industry 4.0 and the emerging 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels complexity in production scheduling. This is further amplified by integration cyber-physical Internet Things, Artificial Intelligence, human-centric approaches, necessitating more sophisticated optimization methods. paper aims to provide a comprehensive perspective on application metaheuristic algorithms shop scheduling problems within context 5.0. Through systematic review recent literature (2015–2024), we analyze categorize various including Evolutionary Algorithms (EAs), swarm intelligence, hybrid methods, that been applied address complex challenges smart environments. We specifically examine how these handle multiple competing objectives such as makespan minimization, energy efficiency, costs, human-machine collaboration, which are crucial modern industrial settings. Our survey reveals several key findings: 1) metaheuristics demonstrate superior performance handling multi-objective compared standalone algorithms; 2) bio-inspired show promising results addressing environments; 3) tri-objective higher-order warrant in-depth exploration; 4) there an trend towards incorporating human factors sustainability optimization, aligned with principles. Additionally, identify research gaps propose future directions, particularly areas real-time adaptation, sustainability-aware algorithms. provides insights for researchers practitioners field scheduling, offering structured understanding current methodologies evolution from

Language: Английский

Citations

0
A feature based neural network model for distributed flexible flow shop scheduling considering worker and transportation factors DOI
Tianpeng Xu, Fuqing Zhao, Jianlin Zhang

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110917 - 110917

Published: Feb. 1, 2025

Language: Английский

Citations

0
Double Deep Q-Network-Based Solution to a Dynamic, Energy-Efficient Hybrid Flow Shop Scheduling System with the Transport Process DOI Creative Commons
Qinglei Zhang, Han Si,

Jiyun Qin

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 170 - 170

Published: Feb. 28, 2025

In this paper, a dynamic energy-efficient hybrid flow shop (TDEHFSP) scheduling model is proposed, considering random arrivals of new jobs and transport by transfer vehicles. To simultaneously optimise the maximum completion time total energy consumption, co-evolutionary approach (DDQCE) using double deep Q-network (DDQN) introduced, where global local search tasks are assigned to different populations use computational resources. addition, multi-objective NEW heuristic strategy implemented generate an initial population with enhanced convergence diversity. The DDQCE incorporates based on interval ‘left shift’ turn-on/off mechanisms, alongside rescheduling manage disturbances. 36 test instances varying sizes, simplified from excavator boom manufacturing process, designed for comparative experiments traditional algorithms. experimental results demonstrate that achieves 40% more Pareto-optimal solutions compared NSGA-II MOEA/D while requiring 10% less time, confirming algorithm efficiently solves TDEHFSP problem.

Language: Английский

Citations

0
Q-learning based estimation of distribution algorithm for scheduling distributed heterogeneous flexible flow-shop with mixed buffering limitation DOI
Hua Xuan, Qianqian Zheng,

Lin Lv

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 149, P. 110537 - 110537

Published: March 12, 2025

Language: Английский

Citations

0
Hybrid flow shop scheduling with continuous processing and resource threshold constraints: A case of steel plant DOI

Zhangsheng Su,

Chao Deng, Raymond Chiong

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127247 - 127247

Published: March 1, 2025

Language: Английский

Citations

0