Impact of the pheromone trail on the performance of ACO algorithms for solving the car-sequencing problem

C Gagné (), M Gravel (), S Morin and W L Price
Additional contact information
C Gagné: Université du Québec à Chicoutimi
M Gravel: Université du Québec à Chicoutimi
S Morin: Université du Québec à Chicoutimi
W L Price: Université Laval Ste-Foy

Journal of the Operational Research Society, 2008, vol. 59, issue 8, 1077-1090

Abstract: Abstract This paper compares different ant colony optimization algorithms for solving the NP-hard car-sequencing problem, which is of great practical interest. The five algorithms that are compared are the Ant System (AS), the Elitist AS, the Rank-Based AS, the Max–Min AS and the Ant Colony System. These algorithms, which are well known in the literature, differ in the way in which the pheromone trail is managed. The comparative analysis seeks to identify which algorithm best manages the learning process in solving the car-sequencing problem. Moreover, we propose a new structure for the pheromone trail specifically designed to take advantage of the type of constraints found in the car-sequencing problem. The quality of the results obtained with this new form of learning for three problem sets drawn from the literature is superior to that of the best results published and demonstrates the efficiency of this new trail structure.

Keywords: metaheuristic; ant colony optimization; pheromone trail; learning; scheduling; car-sequencing (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.2602361 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:8:d:10.1057_palgrave.jors.2602361

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

DOI: 10.1057/palgrave.jors.2602361

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 ().