Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/103922
DC Field | Value | Language |
---|---|---|
dc.contributor | Faculty of Business | en_US |
dc.contributor | Department of Building and Real Estate | en_US |
dc.creator | Wang, H | en_US |
dc.creator | Yi, W | en_US |
dc.creator | Liu, Y | en_US |
dc.date.accessioned | 2024-01-10T02:41:28Z | - |
dc.date.available | 2024-01-10T02:41:28Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/103922 | - |
dc.language.iso | en | en_US |
dc.publisher | American Institute of Mathematical Sciences | en_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.rights | The 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.subject | Infrastructure management | en_US |
dc.subject | Worker assignment | en_US |
dc.subject | Integer programming | en_US |
dc.subject | Machine learning | en_US |
dc.title | Optimal assignment of infrastructure construction workers | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 4178 | en_US |
dc.identifier.epage | 4190 | en_US |
dc.identifier.volume | 30 | en_US |
dc.identifier.issue | 11 | en_US |
dc.identifier.doi | 10.3934/era.2022211 | en_US |
dcterms.abstract | Worker 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Electronic research archive, 2022, v. 30, no. 11, p. 4178-4190 | en_US |
dcterms.isPartOf | Electronic research archive | en_US |
dcterms.issued | 2022 | - |
dc.identifier.isi | WOS:000981666500004 | - |
dc.identifier.scopus | 2-s2.0-85139371442 | - |
dc.identifier.eissn | 2688-1594 | en_US |
dc.description.validate | 202401 bcvc | en_US |
dc.description.oa | Version of Record | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
---|---|---|---|---|
era-30-11-211.pdf | 9.43 MB | Adobe PDF | View/Open |
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