Approximation results for min-max path cover problems in vehicle routing

Zhou Xu, Liang Xu, Chung-Lun Li

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

23 Citations (Scopus)

Abstract

This article studies a min-max path cover problem, which is to determine a set of paths for k capacitated vehicles to service all the customers in a given weighted graph so that the largest path cost is minimized. The problem has wide applications in vehicle routing, especially when the minimization of the latest service completion time is a critical performance measure. We have analyzed four typical variants of this problem, where the vehicles have either unlimited or limited capacities, and they start from either a given depot or any depot of a given depot set. We have developed approximation algorithms for these four variants, which achieve approximation ratios of max{3 -2/k,2}, 5, max{5 -2/k,4}, and 7, respectively. We have also analyzed the approximation hardness of these variants by showing that, unless P = NP, it is impossible for them to achieve approximation ratios less than 4/3, 3/2, 3/2, and 2, respectively. We have further extended the techniques and results developed for this problem to other min-max vehicle routing problems. © 2010 Wiley Periodicals, Inc.
Original languageEnglish
Pages (from-to)728-748
JournalNaval Research Logistics
Volume57
Issue number8
DOIs
Publication statusPublished - Dec 2010
Externally publishedYes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This research was supported in part by the Research Grants Council of Hong Kong under grant numberPolyU5320/10E.

Research Keywords

  • Approximation algorithms
  • Approximation hardness
  • Min-max path cover
  • Vehicle routing

RGC Funding Information

  • RGC-funded

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