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dc.date.accessioned 2019-06-11T14:53:20Z
dc.date.available 2019-06-11T14:53:20Z
dc.date.issued 2013
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/76211
dc.description.abstract The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization (ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) in combinatorial optimization. This paper describes the implementation of an ACO model algorithm known as Elitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop Scheduling Problem (JSSP). We propose a method that seeks to reduce delays designating the operation immediately available, but considering the operations that lack little to be available and have a greater amount of pheromone. The performance of the algorithm was evaluated for problems of JSSP reference, comparing the quality of the solutions obtained regarding the best known solution of the most effective methods. The solutions were of good quality and obtained with a remarkable efficiency by having to make a very low number of objective function evaluations. en
dc.format.extent 72-84 es
dc.language en es
dc.subject ant colony optimization en
dc.subject Heuristic methods es
dc.subject swarm intelligence en
dc.subject combinatorial optimization en
dc.subject job shop scheduling problem en
dc.title An ant colony optimization algorithm for job shop scheduling problem en
dc.type Objeto de conferencia es
sedici.identifier.uri http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/ASAI/07.pdf es
sedici.identifier.issn 1850-2784 es
sedici.creator.person Flórez, Edson es
sedici.creator.person Gómez, Wilfredo es
sedici.creator.person Bautista, Lola es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-sa/4.0/
sedici.date.exposure 2013-09
sedici.relation.event XIV Argentine Symposium on Artificial Intelligence (ASAI) - JAIIO 42 (2013). es
sedici.description.peerReview peer-review es


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