An approximation algorithm for a bottleneck traveling salesman problem

Ming-Yang Kao*, Manan Sanghi

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Consider a truck running along a road. It picks up a load Li at point βi and delivers it at αi, carrying at most one load at a time. The speed on the various parts of the road in one direction is given by f (x) and that in the other direction is given by g (x). Minimizing the total time spent to deliver loads L1, ..., Ln is equivalent to solving the traveling salesman problem (TSP) where the cities correspond to the loads Li with coordinates (αi, βi) and the distance from Li to Lj is given by ∫αiβj f (x) d x if βj ≥ αi and by ∫βjαi g (x) d x if βj < αi. Gilmore and Gomory obtained a polynomial time solution for this TSP [P.C. Gilmore, R.E. Gomory, Sequencing a one state-variable machine: A solvable case of the traveling salesman problem, Operations Research 12 (1964) 655-679]. However, the bottleneck version of the problem (BTSP) was left open. Recently, Vairaktarakis showed that BTSP with this distance metric is NP-complete [G.L. Vairaktarakis, On Gilmore-Gomory's open question for the bottleneck TSP, Operations Research Letters 31 (2003) 483-491]. We provide an approximation algorithm for this BTSP by exploiting the underlying geometry in a novel fashion. We achieve an approximation ratio of (2 + γ) where γ ≥ frac(f (x), g (x)) ≥ frac(1, γ) ∀ x. Note that when f (x) = g (x), the approximation ratio is 3.

Original languageEnglish (US)
Pages (from-to)315-326
Number of pages12
JournalJournal of Discrete Algorithms
Volume7
Issue number3
DOIs
StatePublished - Sep 1 2009

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Keywords

  • Bottleneck traveling salesman problem
  • Job sequencing
  • Nuclear magnetic resonance (NMR) spectroscopy
  • Reconstructing from inaccurate adjacency
  • Two-machine flowshop

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Discrete Mathematics and Combinatorics
  • Computational Theory and Mathematics

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