发表机构
Jiangxi Normal University（江西师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大城市多日旅行行程规划难题，提出集成大语言模型与增强GRASP算法的框架，经实验验证其在行程质量和计算效率上显著优于现有方法，能动态满足多样用户需求。
AI 中文摘要
在大城市地区，由于兴趣点丰富、用户偏好多样以及营业时间等限制，规划多日旅行行程具有挑战性。有效解决方案必须在有限计算时间内动态满足不同旅行者需求，同时优化满意度和可行性。本文引入创新框架，集成大语言模型精确灵活捕捉用户需求，并用增强的贪婪随机自适应搜索程序算法生成可行的多日行程。通过对北京和天津两个真实世界城市数据集的广泛实验，证明该集成方法有效，显著优于现有方法，在行程质量和计算效率上都有提升。
英文摘要
In large urban areas, planning multi-day travel itineraries is challenging due to the abundance of Points of Interest (POIs), diverse user preferences, and constraints such as opening hours. Effective solutions must dynamically accommodate diverse traveler requirements while optimizing for satisfaction and feasibility within limited computation time. This paper addresses these challenges through introducing an innovative framework that integrates Large Language Models (LLMs) to dynamically capture user requirements with precision and flexibility, and an enhanced Greedy Randomized Adaptive Search Procedure (GRASP) algorithm as a well-suited preference-aware planner to generate feasible multi-day itineraries. The effectiveness of our integrated approach is demonstrated through extensive experiments on two real-world urban datasets from Beijing and Tianjin. Our framework significantly outperforms state-of-the-art (SOTA) methods, improving the average total itinerary score by at least 4.52% and 11.09% across 5,040 user cases with diverse preferences in the two datasets. Furthermore, through end-to-end algorithmic enhancements, it achieves notable average improvements of 17.95% and 26.07% in the computed metrics, while also delivering substantial gains in time efficiency -- realizing average performance increases of 4.64% and 25.55% within shorter computation times compared to suboptimal methods that require multiple iterations. These outcomes underscore our method's superiority in delivering both enhanced itinerary quality and computational efficiency over existing methodologies.
Comments37 pages, 16 figures, 7 tables
