Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17820
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dc.contributor.authorAmaldass, N-
dc.contributor.authorLucas, C-
dc.contributor.authorMladenovic, N-
dc.date.accessioned2019-04-01T10:27:10Z-
dc.date.available2016-
dc.date.available2019-04-01T10:27:10Z-
dc.date.issued2017-
dc.identifier.citationYugoslav Journal of Operations Research, 2017, 27 (1), pp. 31 - 45en_US
dc.identifier.issn0354-0243-
dc.identifier.issn1820-743X-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/17820-
dc.description.abstractWe examine the first phase of a known NP-hard 2-stage assembly problem. It consists of sequencing a set of jobs having multiple components to beprocessed. Each job has to be worked on independently on a specific machine. We consider these jobs to form a vector of tasks. Our objective is to schedule jobs on the particular machines in order to minimize the completion time before the second stage starts. We first develop a new mathematical programming formulation of the problem and test it on a small problem instance using an integer programming solver. Then, we develop a heuristic algorithm based on Ant Colony Optimization and Variable Neighborhood Search metaheuristics in order to minimize the total completion time. The performance of our implementation appears to be efficient and effective.en_US
dc.description.sponsorshipNational Research University, Higher School of Economics, Russiaen_US
dc.format.extent31 - 45-
dc.language.isoenen_US
dc.publisherUniversity of Belgradeen_US
dc.subjectvariable neighborhood searchen_US
dc.subjectant colony optimizationen_US
dc.subjectschedulingen_US
dc.subjectinteger programmingen_US
dc.titleA heuristic hybrid framework for vector job schedulingen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.2298/YJOR150416013A-
dc.relation.isPartOfYugoslav Journal of Operations Research-
pubs.issue1-
pubs.publication-statusPublished-
pubs.volume27-
dc.identifier.eissn1820-743X-
Appears in Collections:Dept of Mathematics Research Papers

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