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Scheduling IT Staff at a Bank: A Mathematical Programming Approach

We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to...

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Detalles Bibliográficos
Autores principales: Labidi, M., Mrad, M., Gharbi, A., Louly, M. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3977113/
https://www.ncbi.nlm.nih.gov/pubmed/24772032
http://dx.doi.org/10.1155/2014/768374
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author Labidi, M.
Mrad, M.
Gharbi, A.
Louly, M. A.
author_facet Labidi, M.
Mrad, M.
Gharbi, A.
Louly, M. A.
author_sort Labidi, M.
collection PubMed
description We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to meet the demands for services of an organization while observing workplace regulations and attempting to satisfy individual work preferences. A monthly shift schedule is prepared to determine the shift duties of each staff considering shift coverage requirements, seniority-based workload rules, and staff work preferences. Due to the large number of conflicting constraints, a multiobjective programming model has been proposed to automate the schedule generation process. The suggested mathematical model has been implemented using Lingo software. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules.
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spelling pubmed-39771132014-04-27 Scheduling IT Staff at a Bank: A Mathematical Programming Approach Labidi, M. Mrad, M. Gharbi, A. Louly, M. A. ScientificWorldJournal Research Article We address a real-world optimization problem: the scheduling of a Bank Information Technologies (IT) staff. This problem can be defined as the process of constructing optimized work schedules for staff. In a general sense, it requires the allocation of suitably qualified staff to specific shifts to meet the demands for services of an organization while observing workplace regulations and attempting to satisfy individual work preferences. A monthly shift schedule is prepared to determine the shift duties of each staff considering shift coverage requirements, seniority-based workload rules, and staff work preferences. Due to the large number of conflicting constraints, a multiobjective programming model has been proposed to automate the schedule generation process. The suggested mathematical model has been implemented using Lingo software. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules. Hindawi Publishing Corporation 2014-03-17 /pmc/articles/PMC3977113/ /pubmed/24772032 http://dx.doi.org/10.1155/2014/768374 Text en Copyright © 2014 M. Labidi et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Labidi, M.
Mrad, M.
Gharbi, A.
Louly, M. A.
Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title_full Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title_fullStr Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title_full_unstemmed Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title_short Scheduling IT Staff at a Bank: A Mathematical Programming Approach
title_sort scheduling it staff at a bank: a mathematical programming approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3977113/
https://www.ncbi.nlm.nih.gov/pubmed/24772032
http://dx.doi.org/10.1155/2014/768374
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