ORIGINAL RESEARCH article

Front. Mar. Sci.

Sec. Ocean Solutions

Volume 12 - 2025 | doi: 10.3389/fmars.2025.1614356

This article is part of the Research TopicBig Data and AI for Sustainable Maritime OperationsView all 3 articles

Research on Artificial Intelligence-Driven Container Relocation Problem for Green Ports

Provisionally accepted
  • 1Huizhou University, Huizhou, China
  • 2Guangdong Ocean University, Zhanjiang, Guangdong Province, China

The final, formatted version of the article will be published soon.

Container relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.This study proposes a unified framework integrating an Intelligent Decision-Driven Model (IDDM), an Adaptive Data Generator (ADG), and an Optimization-Learning Closed-Loop Framework (OLCF). The IDDM leverages heuristic search and machine learning within a multi-stage decision mechanism to mitigate the curse of dimensionality; in two-dimensional scenarios involving 50-100 containers, the model achieves an average response time of 9.83 ± 0.12 µs and reduces relocation operations by 61.68%. In three-dimensional experiments at the scale of 10 4 containers, total computation time remains consistently below 60s, satisfying real-time scheduling requirements for automated guided vehicles (AGVs). Additionally, the ADG integrates physical constraints and spatial autocorrelation (Moran's I = 0.3064) to generate high-fidelity, three-dimensional yard configurations at a rate of 10 5 instances per cycle. Predictive models trained on this dataset achieve coefficient-of-determination values of R 2 ≥ 0.85 (peaking at 0.882) across large-scale fully automated, medium-scale semi-automated, and small-scale conventional yard typologies. The OLCF methodology extracts and quantifies 17 key performance indicators. A multi-layer stacked ensemble predicts relocation counts with 90.76% accuracy (R 2 = 0.9139), while a dynamic constraint-weighting mechanism balances movement frequency and energy consumption, thereby enhancing green operational efficiency in high-density container yards. From both theoretical and practical perspectives, this work establishes a multi-stage collaborative optimization pathway by systematically integrating data-driven and model-driven approaches, limits strategy-generation time for 10 5 -container-scale yards to under 60s, and provides a scalable technological paradigm for smart-port development, sustainable logistics, and the attainment of dual-carbon objectives.

Keywords: Green ports, Container relocation problem, artificial intelligence, intelligent decision-driven model, adaptive data generator, Closed-loop framework

Received: 18 Apr 2025; Accepted: 09 Jun 2025.

Copyright: © 2025 Zheng and Sha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Sisi Zheng, Huizhou University, Huizhou, China
Jin Sha, Guangdong Ocean University, Zhanjiang, 130012, Guangdong Province, China

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