Research on artificial intelligence-driven container relocation problem for green ports
IntroductionContainer relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.MethodsThis study proposes a unified framework integrating an Intelligent Decision-Driven Model (IDDM),...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1614356/full |
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| author | Sisi Zheng Jin Sha Yinying Kong Yougan Wang |
| author_facet | Sisi Zheng Jin Sha Yinying Kong Yougan Wang |
| author_sort | Sisi Zheng |
| collection | DOAJ |
| description | IntroductionContainer relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.MethodsThis 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).ResultsThe 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 104 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 105 instances per cycle. Predictive models trained on this dataset achieve coefficient-of-determination values of R2 ≥ 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 (R2 = 0.9139), while a dynamic constraint-weighting mechanism balances movement frequency and energy consumption, thereby enhancing green operational efficiency in high-density container yards.DiscussionFrom 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 105-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. |
| format | Article |
| id | doaj-art-a8c913fef26d44249b2cff00749774f1 |
| institution | DOAJ |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-a8c913fef26d44249b2cff00749774f12025-08-20T02:42:03ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-07-011210.3389/fmars.2025.16143561614356Research on artificial intelligence-driven container relocation problem for green portsSisi Zheng0Jin Sha1Yinying Kong2Yougan Wang3School of Mathematics and Statistics, Huizhou University, Huizhou, Guangdong, ChinaSchool of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, ChinaSchool of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, Guangdong, ChinaSchool of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou, Guangdong, ChinaIntroductionContainer relocation in port yards represents a canonical NP-hard problem, characterized by high-dimensional nonlinear constraints and stringent real-time decision-making requirements.MethodsThis 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).ResultsThe 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 104 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 105 instances per cycle. Predictive models trained on this dataset achieve coefficient-of-determination values of R2 ≥ 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 (R2 = 0.9139), while a dynamic constraint-weighting mechanism balances movement frequency and energy consumption, thereby enhancing green operational efficiency in high-density container yards.DiscussionFrom 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 105-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.https://www.frontiersin.org/articles/10.3389/fmars.2025.1614356/fullgreen portscontainer relocation problemartificial intelligenceintelligent decision-driven modeladaptive data generatorclosed-loop framework |
| spellingShingle | Sisi Zheng Jin Sha Yinying Kong Yougan Wang Research on artificial intelligence-driven container relocation problem for green ports Frontiers in Marine Science green ports container relocation problem artificial intelligence intelligent decision-driven model adaptive data generator closed-loop framework |
| title | Research on artificial intelligence-driven container relocation problem for green ports |
| title_full | Research on artificial intelligence-driven container relocation problem for green ports |
| title_fullStr | Research on artificial intelligence-driven container relocation problem for green ports |
| title_full_unstemmed | Research on artificial intelligence-driven container relocation problem for green ports |
| title_short | Research on artificial intelligence-driven container relocation problem for green ports |
| title_sort | research on artificial intelligence driven container relocation problem for green ports |
| topic | green ports container relocation problem artificial intelligence intelligent decision-driven model adaptive data generator closed-loop framework |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1614356/full |
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