A novel multi-attention reinforcement learning for the scheduling of unmanned shipment vessels (USV) in automated container terminals

Jianxin Zhu, Weidan Zhang, Lean Yu and Xinghai Guo

Omega, 2024, vol. 129, issue C

Abstract: To improve the operating efficiency of container terminals, we investigate a closed-loop scheduling method in an autonomous inter-terminal system that employs unmanned shipment vessels (USVs) to transport containers among operational berths (Dedicated to USVs) in seaport terminals. Our USVs scheduling model is developed by considering energy replenishment, time windows, and berth restrictions, aiming to obtain cost-saving USV transportation solutions and conflict-free paths. To solve this optimization model more efficiently, we propose the multi-attention reinforcement learning (MARL) algorithm by integrating an encoder-decoder framework and an unsupervised auxiliary network. The MARL algorithm provides instant problem-solving capabilities and benefits from extensive offline training. Experimental results demonstrate that our method can obtain efficient solutions for our USVs scheduling problem, and our algorithm outperforms other compared algorithms on computing time and solution accuracy.

Keywords: Container terminal; Unmanned shipment vehicles; Scheduling operation; Reinforcement learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030504832400118X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:129:y:2024:i:c:s030504832400118x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.omega.2024.103152

Access Statistics for this article

Omega is currently edited by B. Lev

More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().