Tuning a parametric Clarke–Wright heuristic via a genetic algorithm

M Battarra, B Golden and D Vigo ()
Additional contact information
M Battarra: Università di Bologna
B Golden: University of Maryland
D Vigo: Università di Bologna

Journal of the Operational Research Society, 2008, vol. 59, issue 11, 1568-1572

Abstract: Abstract Almost all heuristic optimization procedures require the presence of a well-tuned set of parameters. The tuning of these parameters is usually a critical issue and may entail intensive computational requirements. We propose a fast and effective approach composed of two distinct stages. In the first stage, a genetic algorithm is applied to a small subset of representative problems to determine a few robust parameter sets. In the second stage, these sets of parameters are the starting points for a fast local search procedure, able to more deeply investigate the space of parameter sets for each problem to be solved. This method is tested on a parametric version of the Clarke and Wright algorithm and the results are compared with an enumerative parameter-setting approach previously proposed in the literature. The results of our computational testing show that our new parameter-setting procedure produces results of the same quality as the enumerative approach, but requires much shorter computational time.

Keywords: vehicle routing; heuristics; genetic algorithms (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2602488 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:59:y:2008:i:11:d:10.1057_palgrave.jors.2602488

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2602488

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().