Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171767
Title: A machine learning-based approach for multi-AGV dispatching at automated container terminals
Authors: Gao, Yinping
Chen, Chun-Hsien
Chang, Daofang
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Gao, Y., Chen, C. & Chang, D. (2023). A machine learning-based approach for multi-AGV dispatching at automated container terminals. Journal of Marine Science and Engineering, 11(7), 1407-. https://dx.doi.org/10.3390/jmse11071407
Journal: Journal of Marine Science and Engineering 
Abstract: The dispatching of automated guided vehicles (AGVs) is essential for efficient horizontal transportation at automated container terminals. Effective planning of AGV transportation can reduce equipment energy consumption and shorten task completion time. Multiple AGVs transport containers between storage blocks and vessels, which can be regarded as the supply sides and demand points of containers. To meet the requirements of shipment in terms of timely and high-efficient delivery, multiple AGVs should be dispatched to deliver containers, which includes assigning tasks and selecting paths. A contract net protocol (CNP) is employed for task assignment in a multiagent system, while machine learning provides a logical alternative, such as Q-learning (QL), for complex path planning. In this study, mathematical models for multi-AGV dispatching are established, and a QL-CNP algorithm is proposed to tackle the multi-AGV dispatching problem (MADP). The distribution of traffic load is balanced for multiple AGVs performing tasks in the road network. The proposed model is validated using a Gurobi solver with a small experiment. Then, QL-CNP is used to conduct experiments with different sizes. The other algorithms, including Dijkstra, GA, and PSO, are also compared with the QL-CNP algorithm. The experimental results demonstrate the superiority of the proposed QL-CNP when addressing the MADP.
URI: https://hdl.handle.net/10356/171767
ISSN: 2077-1312
DOI: 10.3390/jmse11071407
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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