Analyzing the computing time to solve single row facility layout problems by simulated annealing in a Python framework

dc.contributor.authorMiccoli, Alexandre
dc.contributor.authorHanne, Thomas
dc.contributor.authorDornberger, Rolf
dc.date.accessioned2025-03-07T12:18:15Z
dc.date.issued2023
dc.description.abstractThe goal of this paper is to assess the Python computing time to solve a single row facility layout problem (SRFLP) by Simulated Annealing. The optimization problem is introduced, systematically modelled and then optimized numerically using a particular Python framework. The computing time and the results of experiments with various problem sizes and parameters are analyzed and discussed.
dc.description.urihttps://www.ismsi.org/ismsi2023.html
dc.event2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
dc.event.end2023-04-24
dc.event.start2023-04-23
dc.identifier.doihttps://doi.org/10.1145/3596947.3596953
dc.identifier.isbn978-1-4503-9992-0
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/48176
dc.identifier.urihttps://doi.org/10.26041/fhnw-10891
dc.language.isoen
dc.publisherACM
dc.relation.ispartofProceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.spatialNew York
dc.subject.ddc330 - Wirtschaft
dc.titleAnalyzing the computing time to solve single row facility layout problems by simulated annealing in a Python framework
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaft FHNWde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.openAccessCategoryHybrid
fhnw.pagination66-72
fhnw.publicationStatePublished
relation.isAuthorOfPublication2bbac69c-8f5f-4248-b3b7-0c3f5792157d
relation.isAuthorOfPublication35d8348b-4dae-448a-af2a-4c5a4504da04
relation.isAuthorOfPublication64196f63-c326-4e10-935d-6776cc91354c
relation.isAuthorOfPublication.latestForDiscovery2bbac69c-8f5f-4248-b3b7-0c3f5792157d
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