Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/103922
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorFaculty of Businessen_US
dc.contributorDepartment of Building and Real Estateen_US
dc.creatorWang, Hen_US
dc.creatorYi, Wen_US
dc.creatorLiu, Yen_US
dc.date.accessioned2024-01-10T02:41:28Z-
dc.date.available2024-01-10T02:41:28Z-
dc.identifier.urihttp://hdl.handle.net/10397/103922-
dc.language.isoenen_US
dc.publisherAmerican Institute of Mathematical Sciencesen_US
dc.rights©2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0).en_US
dc.rightsThe following publication Wang, H., Yi, W., & Liu, Y. (2022). Optimal assignment of infrastructure construction workers. Electronic Research Archive, 30(11), 4178-4190 is available at https://doi.org/10.3934/era.2022211.en_US
dc.subjectInfrastructure managementen_US
dc.subjectWorker assignmenten_US
dc.subjectInteger programmingen_US
dc.subjectMachine learningen_US
dc.titleOptimal assignment of infrastructure construction workersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage4178en_US
dc.identifier.epage4190en_US
dc.identifier.volume30en_US
dc.identifier.issue11en_US
dc.identifier.doi10.3934/era.2022211en_US
dcterms.abstractWorker assignment is a classic topic in infrastructure construction. In this study, we developed an integer optimization model to help decision-makers make optimal worker assignment plans while maximizing the daily productivity of all workers. Our proposed model considers the professional skills and physical fitness of workers. Using a real-world dataset, we adopted a machine learning method to estimate the maximum working tolerance time for different workers to carry out different jobs. The real-world dataset also demonstrates the effectiveness of our optimization model. Our work can help project managers achieve efficient management and save labor costs.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationElectronic research archive, 2022, v. 30, no. 11, p. 4178-4190en_US
dcterms.isPartOfElectronic research archiveen_US
dcterms.issued2022-
dc.identifier.isiWOS:000981666500004-
dc.identifier.scopus2-s2.0-85139371442-
dc.identifier.eissn2688-1594en_US
dc.description.validate202401 bcvcen_US
dc.description.oaVersion of Recorden_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextHong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
era-30-11-211.pdf9.43 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

94
Citations as of May 11, 2025

Downloads

47
Citations as of May 11, 2025

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.