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

A Systematic Study on Reinforcement Learning Based Applications DOI Creative Commons

Keerthana Sivamayilvelan,

R Elakkiya,

Belqasem Aljafari

и другие.

Energies, Год журнала: 2023, Номер 16(3), С. 1512 - 1512

Опубликована: Фев. 3, 2023

We have analyzed 127 publications for this review paper, which discuss applications of Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural language processing (NLP), internet things security, recommendation systems, finance, and energy management. The optimization use is critical today’s environment. mainly focus on the RL application Traditional rule-based systems a set predefined rules. As result, they may become rigid unable to adjust changing situations or unforeseen events. can overcome these drawbacks. learns by exploring environment randomly based experience, it continues expand its knowledge. Many researchers are working RL-based management (EMS). utilized such as optimizing smart buildings, hybrid automobiles, grids, managing renewable resources. contributes achieving net zero carbon emissions sustainable In context technology, be optimize regulation building heating, ventilation, air conditioning (HVAC) reduce consumption while maintaining comfortable atmosphere. EMS accomplished teaching an agent make judgments sensor data, temperature occupancy, modify HVAC system settings. has proven beneficial lowering usage buildings active research area buildings. used electric vehicles (HEVs) learning optimal control policy maximize battery life fuel efficiency. acquired remarkable position gaming applications. majority security-related operate simulated recommender provide good suggestions accuracy diversity. This article assists novice comprehending foundations reinforcement

Язык: Английский

Процитировано

67

Job shop smart manufacturing scheduling by deep reinforcement learning DOI Creative Commons
Julio C. Serrano-Ruiz, Josefa Mula, Raúl Poler

и другие.

Journal of Industrial Information Integration, Год журнала: 2024, Номер 38, С. 100582 - 100582

Опубликована: Фев. 9, 2024

Smart manufacturing scheduling (SMS) requires a high degree of flexibility to successfully cope with changes in operational decision level planning processes today's production environments, which are usually subject uncertainty. In such unique and complex scenario as the real job shop, modelling SMS Markov process (MDP), its approach by deep reinforcement learning (DRL), is research field growing interest given characteristics. It allows us consider achieving levels promoting automation, autonomy making, ability act time when faced disturbances disruptions highly dynamic environment. This paper addresses problem quasi-realistic shop environment characterised machines receiving jobs from buffers that accumulate numerous using wide variety parts multimachine routes diverse number operation phases developing digital twin based on MDP DRL methodology. approached by: OpenAI Gym; designing an observation space 18 features; action composed three priority heuristic rules; shaping single reward function multi-objective characteristic; implementation proximal policy optimisation (PPO) algorithm Stable Baselines 3 library. approach, dubbed smart (JS-SMS), deterministic formulation implementation. The model subjected validation comparing it several best-known rules. main findings this methodology allow replicate, great extent, positive aspects rules mitigate negative ones, achieves more balanced behaviour most measures established performance indicators outperforms perspective. Finally, further oriented stochastic approaches address reality Industry 4.0 context.

Язык: Английский

Процитировано

18

HGNP: A PCA-based heterogeneous graph neural network for a family distributed flexible job shop DOI

Jiake Li,

Junqing Li, Ying Xu

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110855 - 110855

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

6

Solving flexible job shop scheduling problems via deep reinforcement learning DOI
Erdong Yuan, Liejun Wang, Shuli Cheng

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 245, С. 123019 - 123019

Опубликована: Дек. 29, 2023

Язык: Английский

Процитировано

32

An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem DOI
Jiang‐Ping Huang, Liang Gao, Xinyu Li

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 121756 - 121756

Опубликована: Сен. 27, 2023

Язык: Английский

Процитировано

29

Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems DOI
Lei Yue, Kai Peng, Linshan Ding

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 90, С. 101660 - 101660

Опубликована: Июль 18, 2024

Язык: Английский

Процитировано

12

Reinforcement Learning in Reliability and Maintenance Optimization: A Tutorial DOI
Qin Zhang, Yu Liu, Yisha Xiang

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 251, С. 110401 - 110401

Опубликована: Июль 28, 2024

Язык: Английский

Процитировано

11

A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals DOI Open Access
Jiang‐Ping Huang, Liang Gao, Xinyu Li

и другие.

Computers & Industrial Engineering, Год журнала: 2023, Номер 185, С. 109650 - 109650

Опубликована: Окт. 4, 2023

Язык: Английский

Процитировано

20

Joint scheduling optimisation method for the machining and heat-treatment of hydraulic cylinders based on improved multi-objective migrating birds optimisation DOI
Xixing Li, Qingqing Zhao, Hongtao Tang

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 170 - 191

Опубликована: Фев. 8, 2024

Язык: Английский

Процитировано

8

A multidimensional probabilistic model based evolutionary algorithm for the energy-efficient distributed flexible job-shop scheduling problem DOI

Zi-Qi Zhang,

Ying Li, Bin Qian

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108841 - 108841

Опубликована: Июнь 18, 2024

Язык: Английский

Процитировано

6