With the deepening of global economic integration, maritime logistics has become the core pillar of international trade, carrying more than 80% of global trade volume. Among the factors influencing efficiency, the selection of vessel unloading paths directly affects operational efficiency and logistics costs, and is therefore a key determinant of competitiveness in maritime trade. At present, unloading path selection for vessels largely relies on manual experience, which often leads to inefficiency and redundant labor. To address the problem of optimizing unloading paths in maritime logistics, this study proposes an intelligent search algorithm that integrates an enumeration method with a cargo volume constraint principle. The approach exhaustively explores all possible unloading schemes and incorporates both the shortest path principle and destination-specific cargo constraints, thereby achieving an unloading path planning solution that is both economical and practical. Experimental results demonstrate that the algorithm can effectively reduce transportation costs and improve operational efficiency for maritime enterprises, offering a new perspective for intelligent logistics management under the trend of digital transformation.
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