ABSTRACT

Activity Chain Optimization (ACO) is the task of finding a minimum-cost tour that visits exactly one location for each required activity while respecting time window constraints. We develop an exact algorithm that efficiently solves the ACO problem in all practical cases that involve hundreds of locations offering up to 10–15 activities and returns the optimal route with minimal time spent traveling and waiting. We also introduce a greedy heuristic that simulates human decision-making for comparison. Our experimental results highlight the practical significance of our work as we can reduce travel and wait times on 45 realistic Budapest inner-city routing problems by 16.65% on average compared to our baseline. Our algorithms’ computational and memory requirements for solving practical ACO instances are shown to be low enough to be employed on embedded devices, e.g. smartphones and navigation systems.

摘要

活动链优化（ACO）的任务是找到一种成本最低的游览路线，该游览路线在遵守时间窗约束的同时，为每个所需的活动恰好访问了一个位置。我们开发了一种精确的算法，可以在涉及数百个地点的所有实际情况下有效解决ACO问题，提供多达10–15个活动，并以最少的旅行和等待时间返回最佳路线。我们还介绍了一种贪婪的启发式方法，该方法可模拟人的决策进行比较。我们的实验结果突显了我们工作的实际意义，因为与基线相比，我们可以将45个现实的布达佩斯市内城市路线问题的出行和等待时间平均减少16.65％。