The Trigger Arc Traveling Salesman Problem (TA-TSP) extends the classical TSP by introducing dynamic arc costs that change when specific "trigger" arcs are traversed, modeling scenarios such as warehouse operations with compactable storage systems. This paper introduces a GRASP-based metaheuristic that combines multiple construction heuristics with a multi-neighborhood local search. The construction phase uses mixed-integer programming (MIP) techniques to transform the TA-TSP into a sequence of tailored TSP instances, while the improvement phase applies 2-Opt, Swap, and Relocate operators. Computational experiments on MESS 2024 competition instances achieved average optimality gaps of 0.77% and 0.40% relative to the best-known solutions within a 60-second limit. On smaller, synthetically generated datasets, the method produced solutions 11.3% better than the Gurobi solver under the same time constraints. The algorithm finished in the top three at MESS 2024, demonstrating its suitability for real-time routing applications with state-dependent travel costs.

翻译：触发弧旅行商问题（TA-TSP）通过引入动态弧成本扩展了经典TSP，该成本在遍历特定“触发”弧时发生变化，可用于建模具有可压缩存储系统的仓库作业等场景。本文提出一种基于GRASP的元启发式算法，将多种构建启发式与多邻域局部搜索相结合。构建阶段采用混合整数规划（MIP）技术将TA-TSP转化为一系列定制化的TSP实例，改进阶段则应用2-Opt、Swap和Relocate算子。在MESS 2024竞赛实例上的计算实验表明，在60秒时限内，算法相对于已知最优解的平均最优性差距为0.77%和0.40%。在较小的合成数据集上，该方法在相同时间限制下获得的解比Gurobi求解器优11.3%。该算法在MESS 2024竞赛中位列前三，证明了其在具有状态相关旅行成本的实时路径规划应用中的适用性。