In ports, a variety of tasks are carried out, and scheduling these tasks is crucial due to its significant impact on productivity, making the generation of precise plans essential. This study proposes a method to solve the Quay Crane Scheduling Problem (QCSP), a representative task scheduling problem in ports known to be NP-Hard, more quickly and accurately. First, the study suggests a method to create more accurate work plans for Quay Cranes (QCs) by learning from actual port data to accurately predict the working speed of QCs. Next, a Surrogate Model is proposed by combining a Machine Learning (ML) model with a Genetic Algorithm (GA), which is widely used to solve complex optimization problems, enabling faster and more precise exploration of solutions. Unlike methods that use fixed-dimensional chromosome encoding, the proposed methodology can provide solutions for encodings of various dimensions. To validate the performance of the newly proposed methodology, comparative experiments were conducted, demonstrating faster search speeds and improved fitness scores. The method proposed in this study can be applied not only to QCSP but also to various NP-Hard problems, and it opens up possibilities for the further development of advanced search algorithms by combining heuristic algorithms with ML models.

翻译：在港口作业中，各类任务的调度对生产效率具有重大影响，因此制定精确的作业计划至关重要。本研究针对港口典型任务调度问题——已知为NP-Hard问题的码头起重机调度问题，提出了一种更快速、更精确的求解方法。首先，本研究提出通过实际港口数据学习以准确预测码头起重机作业速度的方法，从而制定更精确的起重机作业计划。其次，通过将机器学习模型与广泛用于求解复杂优化问题的遗传算法相结合，提出了代理模型，该模型能够更快速、更精确地探索解空间。与采用固定维度染色体编码的方法不同，所提方法可为不同维度的编码提供解决方案。为验证新方法的性能，本研究进行了对比实验，结果表明该方法具有更快的搜索速度和更优的适应度评分。本研究所提出的方法不仅适用于码头起重机调度问题，还可应用于各类NP-Hard问题，同时为启发式算法与机器学习模型的结合开辟了高级搜索算法进一步发展的可能性。