A multiple-level variable neighborhood search approach to the orienteering problem

Yun Chia Liang, Sadan Kulturel-Konak, Min Hua Lo

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

A multiple-level variable neighborhood search (ML-VNS) approach is developed for the orienteering problem (OP) which maximizes the reward collected from visited sites while satisfying predetermined constraints. The ML-VNS approach remedies situations in which the information accumulated during the search process of an individual instance is not shared with the search processes in other instances with different constraint levels. New large-sized OPs have been defined. Results of the ML-VNS approach show promise when compared with previous tabu search (TS) algorithm and probabilistic solution discovery algorithm (PSDA).

Original languageEnglish (US)
Pages (from-to)238-247
Number of pages10
JournalJournal of Industrial and Production Engineering
Volume30
Issue number4
DOIs
StatePublished - Sep 30 2013

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

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abstract = "A multiple-level variable neighborhood search (ML-VNS) approach is developed for the orienteering problem (OP) which maximizes the reward collected from visited sites while satisfying predetermined constraints. The ML-VNS approach remedies situations in which the information accumulated during the search process of an individual instance is not shared with the search processes in other instances with different constraint levels. New large-sized OPs have been defined. Results of the ML-VNS approach show promise when compared with previous tabu search (TS) algorithm and probabilistic solution discovery algorithm (PSDA).",
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A multiple-level variable neighborhood search approach to the orienteering problem. / Liang, Yun Chia; Kulturel-Konak, Sadan; Lo, Min Hua.

In: Journal of Industrial and Production Engineering, Vol. 30, No. 4, 30.09.2013, p. 238-247.

Research output: Contribution to journalArticle

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