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Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm

Numerous studies on project scheduling only consider a single factor, which fails to reflect the actual environment of project operations. In light of this issue, the article synthesizes multiple perspectives and proposes a multi-skill resource-based multi-modal project scheduling problem (MRCMPSP)....

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Detalles Bibliográficos
Autores principales: Peng, Jun Long, Liu, Xiao, Peng, Chao, Shao, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613272/
https://www.ncbi.nlm.nih.gov/pubmed/37898683
http://dx.doi.org/10.1038/s41598-023-45970-y
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author Peng, Jun Long
Liu, Xiao
Peng, Chao
Shao, Yu
author_facet Peng, Jun Long
Liu, Xiao
Peng, Chao
Shao, Yu
author_sort Peng, Jun Long
collection PubMed
description Numerous studies on project scheduling only consider a single factor, which fails to reflect the actual environment of project operations. In light of this issue, the article synthesizes multiple perspectives and proposes a multi-skill resource-based multi-modal project scheduling problem (MRCMPSP). This problem is described, modeled, and solved using the resource capability matrix and other constraints to minimize the project duration. To effectively solve MRCMPSP and enrich scheduling algorithms, the paper selects the hybrid quantum algorithm (HQPSO) based on the quantum particle swarm algorithm (QPSO). The HQPSO introduces various improvements such as the JAYA optimization search to improve the algorithm's performance. In order to verify the generality, superiority, and effectiveness of the algorithm, independent operation comparison experiments and practical application experiments of the algorithm are designed based on different case sizes and resource quantities. The experimental results demonstrate that the proposed algorithm has superior convergence performance and solution accuracy and can provide an effective scheduling solution for real cases. Additionally, the article provides targeted management suggestions based on the research findings. Overall, this study contributes a novel mathematical model, solution algorithm, optimization strategies, and managerial insights, advancing the field of project management research.
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spelling pubmed-106132722023-10-30 Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm Peng, Jun Long Liu, Xiao Peng, Chao Shao, Yu Sci Rep Article Numerous studies on project scheduling only consider a single factor, which fails to reflect the actual environment of project operations. In light of this issue, the article synthesizes multiple perspectives and proposes a multi-skill resource-based multi-modal project scheduling problem (MRCMPSP). This problem is described, modeled, and solved using the resource capability matrix and other constraints to minimize the project duration. To effectively solve MRCMPSP and enrich scheduling algorithms, the paper selects the hybrid quantum algorithm (HQPSO) based on the quantum particle swarm algorithm (QPSO). The HQPSO introduces various improvements such as the JAYA optimization search to improve the algorithm's performance. In order to verify the generality, superiority, and effectiveness of the algorithm, independent operation comparison experiments and practical application experiments of the algorithm are designed based on different case sizes and resource quantities. The experimental results demonstrate that the proposed algorithm has superior convergence performance and solution accuracy and can provide an effective scheduling solution for real cases. Additionally, the article provides targeted management suggestions based on the research findings. Overall, this study contributes a novel mathematical model, solution algorithm, optimization strategies, and managerial insights, advancing the field of project management research. Nature Publishing Group UK 2023-10-28 /pmc/articles/PMC10613272/ /pubmed/37898683 http://dx.doi.org/10.1038/s41598-023-45970-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Peng, Jun Long
Liu, Xiao
Peng, Chao
Shao, Yu
Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title_full Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title_fullStr Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title_full_unstemmed Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title_short Multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
title_sort multi-skill resource-constrained multi-modal project scheduling problem based on hybrid quantum algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613272/
https://www.ncbi.nlm.nih.gov/pubmed/37898683
http://dx.doi.org/10.1038/s41598-023-45970-y
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