Abstract The Team Orienteering Problem with Time Windows (TOPTW) is an extension of the well-known Orienteering Problem. Given a set of locations, each one associated with a profit, a service time and a time window, the objective of the TOPTW is to plan a set of routes, over a subset of locations, that maximizes the total collected profit while satisfying travel time limitations and time window constraints. Within this paper, we present an effective neighborhood search for the TOPTW based on (1) the alternation between two different search spaces, a giant tour search space and a route search space, using a powerful splitting algorithm, and (2) the use of a long term memory mechanism to keep high quality routes encountered in elite solutions. We conduct extensive computational experiments to investigate the contribution of these components, and measure the performance of our method on literature benchmarks. Our approach outperforms state-of-the-art algorithms in terms of overall solution quality and computational time. It finds the current best known solutions, or better ones, for 89 % of the literature instances within reasonable runtimes. Moreover, it is able to achieve better average deviation than state-of-the-art algorithms within shorter computation times. Moreover, new improvements for 57 benchmark instances were found.

中文翻译：

---

具有时间窗的团队定向问题的具有最优分割和自适应记忆的有效邻域搜索

摘要 时间窗团队定向问题（TOPTW）是著名定向问题的扩展。给定一组位置，每个位置都与利润、服务时间和时间窗口相关联，TOPTW 的目标是在位置子集上规划一组路线，在满足旅行时间的同时最大化总收集利润限制和时间窗口限制。在本文中，我们提出了一种有效的 TOPTW 邻域搜索，基于 (1) 两个不同搜索空间之间的交替，一个巨大的旅游搜索空间和一个路线搜索空间，使用强大的分割算法，以及 (2) 使用一种长期记忆机制，以保持精英解决方案中遇到的高质量路线。我们进行了广泛的计算实验来研究这些组件的贡献，并衡量我们的方法在文献基准上的表现。我们的方法在整体解决方案质量和计算时间方面优于最先进的算法。它在合理的运行时间内为 89% 的文献实例找到了当前最著名的解决方案或更好的解决方案。此外，它能够在更短的计算时间内实现比最先进算法更好的平均偏差。此外，还发现了 57 个基准实​​例的新改进。它能够在更短的计算时间内实现比最先进算法更好的平均偏差。此外，还发现了 57 个基准实​​例的新改进。它能够在更短的计算时间内实现比最先进算法更好的平均偏差。此外，还发现了 57 个基准实​​例的新改进。