Hierarchical planning and scheduling for bulk ports via network flow and deep reinforcement learning-guided constraint programming
Hierarchical planning and scheduling for bulk ports via network flow and deep reinforcement learning-guided constraint programming
In this research, an integrated inbound and outbound operational planning and scheduling problem is addressed for complex and large bulk ports. The practice of moving homogeneous dry bulk cargoes on a fixed terminal is changing as raw materials of different types are transported from/to the same terminals. It raises a new research challenge where unloading, stacking, reclaiming, conveying and loading operations must be coordinated to import/export blended products according to the tight specifications of customers. This paper aims to maximise resource utilisation and to satisfy demands as early as possible. The essence of the problem is to design the routing of product flows throughout the port logistics network such that supply and demand are matched optimally. This study presents a new framework that enables the modelling of the planning part as a multi-commodity flow problem and the scheduling part as a constraint programming (CP) problem. A novel dual-engine optimisation method that synergistically combines CP with deep reinforcement learning (DRL) is proposed to accelerate the scheduling phase. The method leverages DRL agents to fix key variables, thereby effectively accelerating the optimisation process of the CP solver. Comprehensive numerical experiments are conducted on real data sets as well as instances derived from real scenarios to validate the effectiveness of the proposed approach, demonstrating significant improvements in port scheduling efficiency. Additionally, strategic management analyses offer actionable insights to support decision-making in bulk port operations. The proposed methods provide a generalised methodology adaptable to a broad range of complex combinatorial optimisation problems in port logistics and beyond, paving the way for more intelligent and sustainable dry bulk port management.
Constraint programming, Deep reinforcement learning, Dry bulk cargo port, Port management, Yard equipment scheduling
Lu, Xuan
05d7dc05-d5a8-4c6c-9194-6f9227da26b8
Zhang, Yu
d3b6a834-b8b1-410e-a0f0-24ad01041c41
Xin, Xuri
83e87647-ed72-4426-8b99-ec0c53efafc4
Yang, Hang
2ba7175d-3578-4747-86d2-ec10ffc14572
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Zhang, Lanbo
46c7b90d-8dff-4436-b61c-26b8e9d86245
Yang, Zaili
e8ff5fad-c312-4643-8e8b-55d2697fed3a
6 February 2026
Lu, Xuan
05d7dc05-d5a8-4c6c-9194-6f9227da26b8
Zhang, Yu
d3b6a834-b8b1-410e-a0f0-24ad01041c41
Xin, Xuri
83e87647-ed72-4426-8b99-ec0c53efafc4
Yang, Hang
2ba7175d-3578-4747-86d2-ec10ffc14572
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Zhang, Lanbo
46c7b90d-8dff-4436-b61c-26b8e9d86245
Yang, Zaili
e8ff5fad-c312-4643-8e8b-55d2697fed3a
Lu, Xuan, Zhang, Yu, Xin, Xuri, Yang, Hang, Li, Huanhuan, Zhang, Lanbo and Yang, Zaili
(2026)
Hierarchical planning and scheduling for bulk ports via network flow and deep reinforcement learning-guided constraint programming.
Transportation Research Part E: Logistics and Transportation Review, 209, [104714].
(doi:10.1016/j.tre.2026.104714).
Abstract
In this research, an integrated inbound and outbound operational planning and scheduling problem is addressed for complex and large bulk ports. The practice of moving homogeneous dry bulk cargoes on a fixed terminal is changing as raw materials of different types are transported from/to the same terminals. It raises a new research challenge where unloading, stacking, reclaiming, conveying and loading operations must be coordinated to import/export blended products according to the tight specifications of customers. This paper aims to maximise resource utilisation and to satisfy demands as early as possible. The essence of the problem is to design the routing of product flows throughout the port logistics network such that supply and demand are matched optimally. This study presents a new framework that enables the modelling of the planning part as a multi-commodity flow problem and the scheduling part as a constraint programming (CP) problem. A novel dual-engine optimisation method that synergistically combines CP with deep reinforcement learning (DRL) is proposed to accelerate the scheduling phase. The method leverages DRL agents to fix key variables, thereby effectively accelerating the optimisation process of the CP solver. Comprehensive numerical experiments are conducted on real data sets as well as instances derived from real scenarios to validate the effectiveness of the proposed approach, demonstrating significant improvements in port scheduling efficiency. Additionally, strategic management analyses offer actionable insights to support decision-making in bulk port operations. The proposed methods provide a generalised methodology adaptable to a broad range of complex combinatorial optimisation problems in port logistics and beyond, paving the way for more intelligent and sustainable dry bulk port management.
Text
Hierarchical Planning and Scheduling-Accepted version
- Accepted Manuscript
More information
Accepted/In Press date: 22 January 2026
e-pub ahead of print date: 6 February 2026
Published date: 6 February 2026
Additional Information:
Publisher Copyright:
© 2026 Elsevier Ltd.
Keywords:
Constraint programming, Deep reinforcement learning, Dry bulk cargo port, Port management, Yard equipment scheduling
Identifiers
Local EPrints ID: 511432
URI: http://eprints.soton.ac.uk/id/eprint/511432
ISSN: 1366-5545
PURE UUID: b7c84df2-fa37-45b7-882d-996dfe238c98
Catalogue record
Date deposited: 14 May 2026 16:37
Last modified: 15 May 2026 02:13
Export record
Altmetrics
Contributors
Author:
Xuan Lu
Author:
Yu Zhang
Author:
Xuri Xin
Author:
Hang Yang
Author:
Huanhuan Li
Author:
Lanbo Zhang
Author:
Zaili Yang
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics