Learning-based Bi-level Multi-objective Optimization for Container Stowage Planning Problem

This study addresses the Container Stowage Planning Problem (CSPP) from a perspective of vessel-yard collaboration. A bi-level multi-objective evolutionary algorithm based on NSGA-II is proposed, in which the upper-level assigns the containers to bays and the lower-level decides specific position for each container. The algorithm employs adaptive evolutionary strategies to enhance diversity. The result of extensive experiments demonstrates the superiority of the proposed method and practical value for real-world scale CSPP in container terminal.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/cec65147.2025.11043072
Primary Topic
Maritime Ports and Logistics
Type
article
Field-Weighted Citation Impact
0.00

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article

Learning-based Bi-level Multi-objective Optimization for Container Stowage Planning Problem

Minghao Guo, Yun Dong
Maritime Ports and Logistics
article

Learning-based Bi-level Multi-objective Optimization for Container Stowage Planning Problem

Minghao Guo, Yun Dong
article en

Abstract

This study addresses the Container Stowage Planning Problem (CSPP) from a perspective of vessel-yard collaboration. A bi-level multi-objective evolutionary algorithm based on NSGA-II is proposed, in which the upper-level assigns the containers to bays and the lower-level decides specific position for each container. The algorithm employs adaptive evolutionary strategies to enhance diversity. The result of extensive experiments demonstrates the superiority of the proposed method and practical value for real-world scale CSPP in container terminal.

Northeastern University (CN)
National Natural Science Foundation of China
Clean water and sanitation
Openalex Percentile: Top 79%
Maritime Ports and Logistics
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