Information Sciences, Год журнала: 2025, Номер unknown, С. 122023 - 122023
Опубликована: Фев. 1, 2025
Язык: Английский
Information Sciences, Год журнала: 2025, Номер unknown, С. 122023 - 122023
Опубликована: Фев. 1, 2025
Язык: Английский
Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 134, С. 108699 - 108699
Опубликована: Май 30, 2024
Язык: Английский
Процитировано
5Applied Soft Computing, Год журнала: 2024, Номер 162, С. 111820 - 111820
Опубликована: Июнь 11, 2024
Язык: Английский
Процитировано
5Computers & Operations Research, Год журнала: 2024, Номер unknown, С. 106914 - 106914
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
5Journal of Manufacturing Systems, Год журнала: 2024, Номер 74, С. 647 - 664
Опубликована: Май 2, 2024
Язык: Английский
Процитировано
4International 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
4Computers & Industrial Engineering, Год журнала: 2024, Номер 196, С. 110507 - 110507
Опубликована: Авг. 22, 2024
Язык: Английский
Процитировано
4Computers & Industrial Engineering, Год журнала: 2024, Номер unknown, С. 110735 - 110735
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
4Journal 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
0International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 33
Опубликована: Май 2, 2025
Язык: Английский
Процитировано
0Journal of Manufacturing Systems, Год журнала: 2024, Номер 77, С. 962 - 989
Опубликована: Ноя. 13, 2024
Язык: Английский
Процитировано
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