Repository logo
 
Publication

A comparison between optimization tools to solve sectorization problem

2022Conference object Open access
Simple item page
dc.contributor.authorTeymourifar, Aydin
dc.contributor.authorRodrigues, Ana Maria
dc.contributor.authorFerreira, José Soeiro
dc.contributor.authorLopes, Cristina
dc.date.accessioned2022-01-25T11:24:36Z
dc.date.available2023-01-01T01:30:33Z
dc.date.issued2022
dc.description.abstractIn sectorization problems, a large district is split into small ones, usually meeting certain criteria. In this study, at first, two single-objective integer programming models for sectorization are presented. Models contain sector centers and customers, which are known beforehand. Sectors are established by assigning a subset of customers to each center, regarding objective functions like equilibrium and compactness. Pulp and Pyomo libraries available in Python are utilised to solve related benchmarks. The problems are then solved using a genetic algorithm available in Pymoo, which is a library in Python that contains evolutionary algorithms. Furthermore, the multi-objective versions of the models are solved with NSGA-II and RNSGA-II from Pymoo. A comparison is made among solution approaches. Between solvers, Gurobi performs better, while in the case of setting proper parameters and operators the evolutionary algorithm in Pymoo is better in terms of solution time, particularly for larger benchmarks.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-92666-3_4pt_PT
dc.identifier.eid85121898105
dc.identifier.isbn9783030926656
dc.identifier.urihttp://hdl.handle.net/10400.14/36554
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectGurobipt_PT
dc.subjectOptimizationpt_PT
dc.subjectPulppt_PT
dc.subjectPymoopt_PT
dc.subjectPyomopt_PT
dc.subjectSectorizationpt_PT
dc.titleA comparison between optimization tools to solve sectorization problempt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage50pt_PT
oaire.citation.startPage40pt_PT
oaire.citation.titleModelling, computation and optimization in information systems and management sciences: proceedings of the 4th international conference on modelling, computation and optimization in information systems and management sciences - MCO 2021pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
40015510.pdf
Size:
310.24 KB
Format:
Adobe Portable Document Format
Download

Collections

CEGE - Documentos de Conferências / Conference Objects