Sengupta Lahari, Mariescu-Istodor Radu, Fränti Pasi
Machine Learning, School of Computing, University of Eastern Finland, Joensuu, Finland.
Comput Brain Behav. 2018;1(3):252-265. doi: 10.1007/s42113-018-0018-0. Epub 2018 Dec 4.
Tour planning is an important part of location-based applications. A tour planner provides an optimized path through places of interests (targets) by minimizing the tour length or by applying some other constraints. It is usually formulated as a travelling salesman problem (TSP) or vehicle routing problem (VRP). In the present study, we focus on how to choose the best starting location in case of an open-loop TSP. We consider three different strategies for selecting the starting location and compare their effectiveness with regard to optimizing tour length. If all targets are visible, most humans tend to start on the convex hull or from the furthest point. However, there are also cases where not all targets are visible beforehand, and the only information given is the bounding box. An optimum tour then typically starts from the corner or the shorter side of the box. Humans also have a strong preference to start from a corner. A good strategy can result in the shortest tour, while a bad strategy can even add 20% to the total tour length.
行程规划是基于位置的应用程序的重要组成部分。行程规划器通过最小化行程长度或应用其他一些约束条件,提供一条经过多个兴趣点(目标)的优化路径。它通常被表述为旅行商问题(TSP)或车辆路径问题(VRP)。在本研究中,我们关注在开环TSP情况下如何选择最佳起始位置。我们考虑三种不同的起始位置选择策略,并比较它们在优化行程长度方面的有效性。如果所有目标都可见,大多数人倾向于从凸包或最远点开始。然而,也存在并非所有目标都能预先看到的情况,此时给出的唯一信息是边界框。那么,最优行程通常从框的角落或较短边开始。人类也强烈倾向于从角落开始。一个好的策略可以得到最短的行程,而一个糟糕的策略甚至可能使总行程长度增加20%。