We present a polynomial-time approximation scheme (PTAS) for the min-max multiple TSP problem in Euclidean space, where multiple traveling salesmen are tasked with visiting a set of $n$ points and the objective is to minimize the maximum tour length. For an arbitrary $\varepsilon > 0$, our PTAS achieves a $(1 + \varepsilon)$-approximation in time $O \big(n ((1/\varepsilon) \log (n/\varepsilon))^{O(1/\varepsilon)} \big)$. Our approach extends Sanjeev Arora's dynamic-programming (DP) PTAS for the Euclidean TSP (https://doi.org/10.1145/290179.290180). Our algorithm introduces a rounding process to balance the allocation of path lengths among the multiple salesman. We analyze the accumulation of error in the DP to prove that the solution is a $(1 + \varepsilon)$-approximation.
翻译:我们为欧几里德空间的微积分多重TSP问题提出了一个多时近似方案(PTAS),由多个旅行推销员负责访问一组n美元点,目标是最大限度地减少最长的导游时间。对于专制的 $@varepsilon > 0美元,我们的PTAS 将达到$(1+\varepsilon)$-apporimation, 时间为$(n)(1/\varepsilon)\log(n/varepsilon) ⁇ O(1/\varepsilon)} ⁇ (o)(1/\varepsilon)\big)$。我们的方法扩展了Sanjeev Arora的动态-programing(DP) Euclidean TSP PTAS (https://doi.org/10.1145/2979.290180) 。我们的算法引入了一个圆形过程,以平衡多销售员之间路径长度的分配。我们分析了DP的累积错误,以证明解决办法是$(1+\\\varepsilon)-ap-apmation。