Poly-logarithmic Approximation Algorithms for Directed Vehicle Routing Problems

  • Authors:
  • Viswanath Nagarajan;R. Ravi

  • Affiliations:
  • Tepper School of Business, Carnegie Mellon University, Pittsburgh PA 15213,;Tepper School of Business, Carnegie Mellon University, Pittsburgh PA 15213,

  • Venue:
  • APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
  • Year:
  • 2007

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Abstract

This paper studies vehicle routing problems on asymmetric metrics. Our starting point is the directed k-TSPproblem: given an asymmetric metric (V,d), a root r茂戮驴 Vand a target k≤ |V|, compute the minimum length tour that contains rand at least kother vertices. We present a polynomial time O(log2n·logk)-approximation algorithm for this problem. We use this algorithm for directed k-TSP to obtain an O(log2n)-approximation algorithm for the directed orienteeringproblem. This answers positively, the question of poly-logarithmic approximability of directed orienteering, an open problem from Blum et al.[2]. The previously best known results were quasi-polynomial time algorithms with approximation guarantees of O(log2k) for directed k-TSP, and O(logn) for directed orienteering (Chekuri & Pal [4]). Using the algorithm for directed orienteering within the framework of Blum et al.[2] and Bansal et al.[1], we also obtain poly-logarithmic approximation algorithms for the directed versions of discounted-reward TSPand the vehicle routing problem with time-windows.