AI驱动的微服务系统优化旅行路线，兼顾成本、时间、偏好与环保。
Optimizing Travel Itineraries with AI Algorithms in a Microservices Architecture: Balancing Cost, Time, Preferences, and Sustainability
- 用遗传算法和机器学习动态规划行程，实时响应用户需求。
- 92%满足用户偏好，95%在预算内，碳排放比传统方案低15%。
- 支持高并发、故障容错，适合追求高效低碳旅行的平台使用。
本研究旨在探讨在微服务架构中应用AI算法如何提升旅行行程的优化效果，涵盖成本、时间、用户偏好及环境可持续性。系统采用机器学习模型进行成本预测与个性化推荐，使用遗传算法优化行程，结合启发式规则评估可持续性。主要评估指标包括延迟、满足用户偏好的能力、成本控制及环境影响。实验结果表明，系统在1000并发用户下平均响应时间为4.5秒，用户偏好满足率达92%，95%的行程在用户预算范围内。系统通过引入绿色出行选项，60%的行程包含环保方案，平均碳排放较传统方案降低15%。遗传算法在100代内以O(g.p.f)时间复杂度实现最优解，每代提升5%解质量，适用于秒级响应的优化场景。系统具备99.9%可用性，支持弹性扩展、异步通信与实时调整。该架构使旅行平台更动态高效，融合AI实现成本控制与环保目标，满足现代旅行业务多元需求。
原文摘要 · Abstract (English)
The objective of this research is how an implementation of AI algorithms in the microservices architecture enhances travel itineraries by cost, time, user preferences, and environmental sustainability. It uses machine learning models for both cost forecasting and personalization, genetic algorithm for optimization of the itinerary, and heuristics for sustainability checking. Primary evaluated parameters consist of latency, ability to satisfy user preferences, cost and environmental concern. The experimental results demonstrate an average of 4.5 seconds of response time on 1000 concurrent users and 92% of user preferences accuracy. The cost efficiency is proved, with 95% of provided trips being within the limits of the budget declared by the user. The system also implements some measures to alleviate negative externalities related to travel and 60% of offered travel plans had green options incorporated, resulting in the average 15% lower carbon emissions than the traditional travel plans offered. The genetic algorithm with time complexity O(g.p.f) provides the optimal solution in 100 generations. Every iteration improves the quality of the solution by 5%, thus enabling its effective use in optimization problems where time is measured in seconds. Finally, the system is designed to be fault-tolerant with functional 99.9% availability which allows the provision of services even when requirements are exceeded. Travel optimization platform is turned dynamic and efficient by this microservices based architecture which provides enhanced scaling, allows asynchronous communication and real time changes. Because of the incorporation of Ai, cost control and eco-friendliness approaches, the system addresses the different user needs in the present days travel business.
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