Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Expanding neighborhood search-GRASP for the probabilistic traveling salesman problem
Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete.
Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete.ORCID iD: 0000-0001-8473-3663
Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida.
2008 (English)In: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480, Vol. 2, no 3, p. 351-361Article in journal (Refereed) Published
Abstract [en]

The Probabilistic Traveling Salesman Problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In probabilistic traveling salesman problem only a subset of potential customers need to be visited on any given instance of the problem. The number of customers to be visited each time is a random variable. In this paper, a variant of the well-known Greedy Randomized Adaptive Search Procedure (GRASP), the Expanding Neighborhood Search-GRASP, is proposed for the solution of the probabilistic traveling salesman problem. expanding neighborhood search-GRASP has been proved to be a very efficient algorithm for the solution of the traveling salesman problem. The proposed algorithm is tested on a numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons with the classic GRASP algorithm and with a Tabu Search algorithm are also presented. Also, a comparison is performed with the results of a number of implementations of the Ant Colony Optimization algorithm from the literature and in six out of ten cases the proposed algorithm gives a new best solution

Place, publisher, year, edition, pages
2008. Vol. 2, no 3, p. 351-361
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Industrial Logistics
Identifiers
URN: urn:nbn:se:ltu:diva-6049DOI: 10.1007/s11590-007-0064-3Local ID: 43e37552-c668-411f-97ee-9cfce8d5e63dOAI: oai:DiVA.org:ltu-6049DiVA, id: diva2:978926
Note
Upprättat; 2008; 20140829 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-04-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Migdalas, Athanasios

Search in DiVA

By author/editor
Migdalas, Athanasios
In the same journal
Optimization Letters
Production Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 7 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf