The Capacitated Team Orienteering Problem (CTOP) is a variant of the well-known Team Orienteering Problem where additional capacity limitation constraints are considered for each vehicle. Solving CTOP consists of organizing a set of routes that maximize the total profit collected from the served customers while taking into consideration the capacity and travel time limitation for each vehicle. In this paper, we propose a variable space search heuristic to solve CTOP. Our algorithm alternates between two search spaces: the giant tour and routes search spaces. We develop a hybrid heuristic as a framework for our algorithm composed of a combination between Greedy Randomized Adaptive Search Procedure and Evolutionary Local Search. Several local search techniques were developed in each search space to improve the quality of the solutions and the giant tours. A dedicated optimal split procedure and a concatenation technique are performed to ensure the link between the search spaces. This approach shows its high performance on the benchmark of CTOP, and proves its competitiveness in comparison to the other heuristic methods available in the literature as it yields to strict improvements with small computational time.

中文翻译：

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有能力的团队定向运动问题的变空间搜索启发式

有能力的团队定向运动问题（CTOP）是众所周知的团队定向运动问题的变体，其中，每辆车都考虑了额外的容量限制约束。解决CTOP包括组织一组路线，以使从服务客户那里获得的总利润最大化，同时考虑每辆车的容量和行驶时间限制。在本文中，我们提出了一种可变空间搜索启发式方法来解决CTOP。我们的算法在两个搜索空间之间交替：巨型游览和路线搜索空间。我们开发了一种混合启发式算法作为我们算法的框架，该算法由贪婪随机自适应搜索过程和进化局部搜索之间的组合组成。在每个搜索空间中开发了几种本地搜索技术，以提高解决方案和巡回演出的质量。执行专用的最佳分割过程和级联技术以确保搜索空间之间的链接。这种方法在CTOP的基准上显示出了很高的性能，并且与文献中提供的其他启发式方法相比，证明了它的竞争力，因为它可以用很少的计算时间进行严格的改进。