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The impact of a new formulation when solving the set covering problem using the ACO metaheuristic

dc.contributor.authorCrawford B.
dc.contributor.authorSoto R.
dc.contributor.authorPalma W.
dc.contributor.authorParedes F.
dc.contributor.authorJohnson F.
dc.contributor.authorNorero E.
dc.date.accessioned2020-09-02T22:15:43Z
dc.date.available2020-09-02T22:15:43Z
dc.date.issued2015
dc.identifier10.1007/978-3-319-18167-7_19
dc.identifier.citation360, , 209-218
dc.identifier.issn21945357
dc.identifier.urihttps://hdl.handle.net/20.500.12728/4166
dc.descriptionThe Set Covering Problem (SCP) is a well-known NP hard discrete optimization problem that has been applied to a wide range of industrial applications, including those involving scheduling, production planning and location problems. The main difficulties when solving the SCP with a metaheuristic approach are the solution infeasibility and set redundancy. In this paper we evaluate a state of the art new formulation of the SCP which eliminates the need to address the infeasibility and set redundancy issues. The experimental results, conducted on a portfolio of SCPs from the Beasley’s OR-Library, show the gains obtained when using a new formulation to solve the SCP using the ACO metaheuristic. © Springer International Publishing Switzerland 2015.
dc.language.isoen
dc.publisherSpringer Verlag
dc.sourceLe Thi H.A.Nguyen N.T.Dinh T.P.
dc.subjectAnt Colony Optimization
dc.subjectMetaheuristics
dc.subjectSet Covering Problem
dc.subjectAlgorithms
dc.subjectAnt colony optimization
dc.subjectArtificial intelligence
dc.subjectInformation management
dc.subjectInformation systems
dc.subjectManagement science
dc.subjectProduction control
dc.subjectRedundancy
dc.subjectDiscrete optimization problems
dc.subjectLocation problems
dc.subjectMeta heuristics
dc.subjectMeta-heuristic approach
dc.subjectProduction Planning
dc.subjectSet covering problem
dc.subjectSet redundancies
dc.subjectState of the art
dc.subjectOptimization
dc.titleThe impact of a new formulation when solving the set covering problem using the ACO metaheuristic
dc.typeConference Paper


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