The Time Dependent (TD) Traveling Salesman Problem (TSP) is a generalization of the TSP which allows one to take traffic conditions into account when planning tours in an urban context, by making the travel time between locations dependent on the departure time instead of being constant. The TD-TSPTW further generalizes this problem by adding Time Window constraints. Existing exact approaches such as Integer Linear Programming and Dynamic Programming usually do not scale well. We therefore introduce a new exact approach based on an anytime extension of A*. We combine this approach with local search, to converge faster towards better solutions, and bounding and time window constraint propagation, to prune parts of the state space. We experimentally compare our approach with state-of-the-art approaches on both TD-TSPTW and TSPTW benchmarks.

时间相关 (TD) 旅行商问题 (TSP) 是 TSP 的推广，它允许人们在规划城市环境中的旅行时考虑交通状况，使地点之间的旅行时间取决于出发时间，而不是由出发时间决定。持续的。TD-TSPTW 通过添加时间窗口约束进一步概括了这个问题。现有的精确方法（例如整数线性规划和动态规划）通常无法很好地扩展。因此，我们引入了一种基于 A* 随时扩展的新精确方法。我们将这种方法与局部搜索相结合，以更快地收敛到更好的解决方案，并结合边界和时间窗口约束传播，以修剪部分状态空间。我们通过实验将我们的方法与 TD-TSPTW 和 TSPTW 基准上最先进的方法进行比较。