用机器学习+遗传算法，更快更准地解决港口岸桥调度难题
A Surrogate Model for Quay Crane Scheduling Problem
- 结合机器学习与遗传算法构建代理模型，动态适应不同规模编码
- 实验显示搜索速度更快，优化结果更优，适应多种调度场景
- 适合研究智能调度、混合优化算法的学者与工程人员参考
在港口中，各类作业任务的调度对生产效率影响显著，精确计划生成至关重要。本文针对典型的港口作业调度难题——岸桥调度问题（QCSP），该问题被证明为NP-Hard，提出一种更快速、更准确的求解方法。首先，通过学习实际港口数据，建立能精准预测岸桥工作速度的模型；其次，提出一种将机器学习模型与遗传算法（GA）融合的代理模型，实现对复杂解空间的高效精确探索。该方法突破传统固定维度染色体编码的限制，可处理多种维度编码方案。通过对比实验验证，所提方法在搜索速度和适应度得分上均表现更优。该方法不仅适用于QCSP，还可推广至其他NP-Hard优化问题，为融合启发式算法与机器学习的先进搜索算法发展提供了新思路。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。