A Review of Scheduling Methods for Multi-AGV Material Handling Systems in Mixed-Model Assembly Workshops DOI Creative Commons

Tianyuan Mao

Frontiers in Sustainable Development, Год журнала: 2025, Номер 5(3), С. 227 - 237

Опубликована: Март 22, 2025

Currently, automobile production in workshops faces demands for multi-variety, small-batch, and rapid delivery. As a key auxiliary link, optimizing the performance of workshop material scheduling system can enhance efficiency economic benefits. With expansion enterprise scale complexity requirements, multi-AGV handling systems have become an effective solution to optimize processes save costs due their parallel collaboration advantages. However, NP-hard nature this problem, traditional exact algorithms often perform poorly when dealing with complex large-scale problems. Therefore, paper explores applications intelligent such as genetic algorithms, artificial neural networks, particle swarm optimization, proposes novel efficient solutions methods mixed-model assembly workshops. In addition, address problem large state space schemes, also discusses potential emerging technologies reinforcement learning. Through these studies, it aims processes, reduce costs, promote development manufacturing industry.

Язык: Английский

LLM Multi-agent Decision Optimization DOI
J. de Curtò, I. de Zarzà, Carlos T. Calafate

и другие.

Smart innovation, systems and technologies, Год журнала: 2025, Номер unknown, С. 3 - 15

Опубликована: Янв. 1, 2025

Процитировано

0
A novel deep self-learning method for flexible job-shop scheduling problems with multiplicity: Deep reinforcement learning assisted the fluid master-apprentice evolutionary algorithm DOI
Linshan Ding, Dan Luo, Mudassar Rauf

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101907 - 101907

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0
Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning DOI Creative Commons
Meng Xu, Yi Mei, Fangfang Zhang

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

0
A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints DOI Creative Commons
Xiaoting Dong, Guangxi Wan, Peng Zeng

и другие.

Complex & Intelligent Systems, Год журнала: 2025, Номер 11(5)

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0
A Review of Scheduling Methods for Multi-AGV Material Handling Systems in Mixed-Model Assembly Workshops DOI Creative Commons

Tianyuan Mao

Frontiers in Sustainable Development, Год журнала: 2025, Номер 5(3), С. 227 - 237

Опубликована: Март 22, 2025

Currently, automobile production in workshops faces demands for multi-variety, small-batch, and rapid delivery. As a key auxiliary link, optimizing the performance of workshop material scheduling system can enhance efficiency economic benefits. With expansion enterprise scale complexity requirements, multi-AGV handling systems have become an effective solution to optimize processes save costs due their parallel collaboration advantages. However, NP-hard nature this problem, traditional exact algorithms often perform poorly when dealing with complex large-scale problems. Therefore, paper explores applications intelligent such as genetic algorithms, artificial neural networks, particle swarm optimization, proposes novel efficient solutions methods mixed-model assembly workshops. In addition, address problem large state space schemes, also discusses potential emerging technologies reinforcement learning. Through these studies, it aims processes, reduce costs, promote development manufacturing industry.

Язык: Английский

Процитировано

0