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Optimizing yard truck deployment at container terminals: a machine learning-enhanced non-dominated sorting genetic algorithm
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DOI：10.1016/j.cie.2025.111610.png)
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Abstract
En 中文
• Proposes an Machine Learning (ML)-enhanced NSGA-2 to optimize yard truck deployment. • Trains ML models with simulation and terminal data to guide solutions. • Balances vessel makespan and truck usage in a bi-objective framework.
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