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Some metaheuristic algorithms for solving multiple cross-functional team selection problems
We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455285/ https://www.ncbi.nlm.nih.gov/pubmed/36092009 http://dx.doi.org/10.7717/peerj-cs.1063 |
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author | Ngo, Son Tung Jaafar, Jafreezal Izzatdin, Aziz Abdul Tong, Giang Truong Bui, Anh Ngoc |
author_facet | Ngo, Son Tung Jaafar, Jafreezal Izzatdin, Aziz Abdul Tong, Giang Truong Bui, Anh Ngoc |
author_sort | Ngo, Son Tung |
collection | PubMed |
description | We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA). |
format | Online Article Text |
id | pubmed-9455285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94552852022-09-09 Some metaheuristic algorithms for solving multiple cross-functional team selection problems Ngo, Son Tung Jaafar, Jafreezal Izzatdin, Aziz Abdul Tong, Giang Truong Bui, Anh Ngoc PeerJ Comput Sci Adaptive and Self-Organizing Systems We can find solutions to the team selection problem in many different areas. The problem solver needs to scan across a large array of available solutions during their search. This problem belongs to a class of combinatorial and NP-Hard problems that requires an efficient search algorithm to maintain the quality of solutions and a reasonable execution time. The team selection problem has become more complicated in order to achieve multiple goals in its decision-making process. This study introduces a multiple cross-functional team (CFT) selection model with different skill requirements for candidates who meet the maximum required skills in both deep and wide aspects. We introduced a method that combines a compromise programming (CP) approach and metaheuristic algorithms, including the genetic algorithm (GA) and ant colony optimization (ACO), to solve the proposed optimization problem. We compared the developed algorithms with the MIQP-CPLEX solver on 500 programming contestants with 37 skills and several randomized distribution datasets. Our experimental results show that the proposed algorithms outperformed CPLEX across several assessment aspects, including solution quality and execution time. The developed method also demonstrated the effectiveness of the multi-criteria decision-making process when compared with the multi-objective evolutionary algorithm (MOEA). PeerJ Inc. 2022-08-09 /pmc/articles/PMC9455285/ /pubmed/36092009 http://dx.doi.org/10.7717/peerj-cs.1063 Text en ©2022 Ngo et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Adaptive and Self-Organizing Systems Ngo, Son Tung Jaafar, Jafreezal Izzatdin, Aziz Abdul Tong, Giang Truong Bui, Anh Ngoc Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title | Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title_full | Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title_fullStr | Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title_full_unstemmed | Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title_short | Some metaheuristic algorithms for solving multiple cross-functional team selection problems |
title_sort | some metaheuristic algorithms for solving multiple cross-functional team selection problems |
topic | Adaptive and Self-Organizing Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9455285/ https://www.ncbi.nlm.nih.gov/pubmed/36092009 http://dx.doi.org/10.7717/peerj-cs.1063 |
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