From Heuristics to Multi-Agent Learning: A Survey of Intelligent Scheduling Methods in Port Seaside Operations

Yaqiong Lv, Jingwen Wang, Zhongyuan Liu and Mingkai Zou ()
Additional contact information
Yaqiong Lv: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
Jingwen Wang: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
Zhongyuan Liu: School of Transport and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Mingkai Zou: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China

Mathematics, 2025, vol. 13, issue 17, 1-57

Abstract: Port seaside scheduling, involving berth allocation, quay crane, and tugboat scheduling, is central to intelligent port operations. This survey reviews and statistically analyzes 152 academic publications from 2000 to 2025 that focus on optimization techniques for port seaside scheduling. The reviewed methods span mathematical modeling and exact algorithms, heuristic and simulation-based approaches, and agent-based and learning-driven techniques. Findings show deterministic models remain mainstream (77% of studies), with uncertainty-aware models accounting for 23%. Heuristic and simulation approaches are most commonly used (60.5%), followed by exact algorithms (21.7%) and agent-based methods (12.5%). While berth and quay crane scheduling have historically been the primary focus, there is growing research interest in tugboat operations, pilot assignment, and vessel routing under navigational constraints. The review traces a clear evolution from static, single-resource optimization to dynamic, multi-resource coordination enabled by intelligent modeling. Finally, emerging trends such as the integration of large language models, green scheduling strategies, and human–machine collaboration are discussed, providing insights and directions for future research and practical implementations.

Keywords: port seaside operations scheduling; exact algorithms; metaheuristic algorithms; reinforcement learning; multi-agent reinforcement learning (MARL); intelligent methods; literature analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/17/2744/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/17/2744/ (text/html)

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:gam:jmathe:v:13:y:2025:i:17:p:2744-:d:1732757

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().