Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop DOI
Biao Zhang, Hongyan Sang, Chao Lu

и другие.

Information Sciences, Год журнала: 2025, Номер unknown, С. 122023 - 122023

Опубликована: Фев. 1, 2025

Язык: Английский

Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning DOI
Lixiang Zhang, Yan Yan, Yaoguang Hu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 134, С. 108699 - 108699

Опубликована: Май 30, 2024

Язык: Английский

Процитировано

5
Scheduling techniques for addressing uncertainties in container ports: A systematic literature review DOI
Wenfeng Li, Lei Cai, Lijun He

и другие.

Applied Soft Computing, Год журнала: 2024, Номер 162, С. 111820 - 111820

Опубликована: Июнь 11, 2024

Язык: Английский

Процитировано

5
Graph neural networks for job shop scheduling problems: A survey DOI Creative Commons
Igor G. Smit, Jianan Zhou, Robbert Reijnen

и другие.

Computers & Operations Research, Год журнала: 2024, Номер unknown, С. 106914 - 106914

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

5
Integrated decision of production scheduling and condition-based maintenance planning for multi-unit systems with variable replacement thresholds DOI
Wenyu Zhang, Xiaohong Zhang, Jie Gan

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 74, С. 647 - 664

Опубликована: Май 2, 2024

Язык: Английский

Процитировано

4
Machine learning in smart production logistics: a review of technological capabilities DOI Creative Commons
Erik Flores-García,

Dong Hoon Kwak,

Yongkuk Jeong

и другие.

International Journal of Production Research, Год журнала: 2024, Номер unknown, С. 1 - 35

Опубликована: Июль 22, 2024

Recent publications underscore the critical implications of adapting to dynamic environments for enhancing performance material and information flows.This study presents a systematic review literature that explores technological capabilities smart production logistics (SPL) when applying machine learning (ML) enhance in environments.This applies inductive theory building extends existing knowledge about SPL three ways.First, it describes role ML advancing across various dimensions, such as time, quality, sustainability, cost.Second, this demonstrates application component technologies (i.e.scanning, storing, interpreting, executing, learning) attain superior SPL.Third, outlines how manufacturing companies can cultivate effectively apply ML.In particular, introduces comprehensive framework establishes foundations SPL, thus facilitating successful integration ML, improvement capabilities.Finally, practical managers staff responsible planning execution tasks, including movement materials factories.

Язык: Английский

Процитировано

4
A machine learning-based simulation metamodeling method for dynamic scheduling in smart manufacturing systems DOI Creative Commons
Erfan Nejati,

Ensieh Ghaedy-Heidary,

Amir Ghasemi

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер 196, С. 110507 - 110507

Опубликована: Авг. 22, 2024

Язык: Английский

Процитировано

4
A novel neighborhood structure for flexible job shop scheduling problem considering Quality-Efficiency coupling effect DOI

Qinglin Zheng,

Wei Dai, Changsheng Peng

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер unknown, С. 110735 - 110735

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

4
Study on the Multi-Equipment Integrated Scheduling Problem of a U-Shaped Automated Container Terminal Based on Graph Neural Network and Deep Reinforcement Learning DOI Creative Commons
Qinglei Zhang, Yi Zhu,

Jiyun Qin

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(2), С. 197 - 197

Опубликована: Янв. 22, 2025

Intelligent Guided Vehicles (IGVs) in U-shaped automated container terminals (ACTs) have longer travel paths than those conventional vertical layout ACTs, and their interactions with double trolley quay cranes (DTQCs) cantilever rail (DCRCs) are more frequent complex, so the scheduling strategy of a traditional ACT cannot easily be applied to ACT. With aim minimizing maximum task completion times within ACT, this study investigates integrated problem DTQCs, IGVs DCRCs under hybrid “loading unloading” mode, expresses as Markovian decision-making process, establishes disjunctive graph model. A deep reinforcement learning algorithm based on neural network combined proximal policy optimization is proposed. To verify superiority proposed models algorithms, instances different scales were stochastically generated compare method several heuristic algorithms. This also analyses idle time equipment two loading unloading modes, results show that mode can enhance operational effectiveness.

Язык: Английский

Процитировано

0
Dynamic scheduling for flexible job-shop with reconfigurable manufacturing cells considering dynamic job arrivals based on deep reinforcement learning DOI
Liang Zheng, Xiaodi Chen, Cunbo Zhuang

и другие.

International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 33

Опубликована: Май 2, 2025

Язык: Английский

Процитировано

0
Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review DOI Creative Commons
Chao Zhang, Max Juraschek, Christoph Herrmann

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 77, С. 962 - 989

Опубликована: Ноя. 13, 2024

Язык: Английский

Процитировано

3