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基于混合量子算法的多技能资源受限多模态项目调度问题

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

作者信息

Peng Jun Long, Liu Xiao, Peng Chao, Shao Yu

机构信息

Changsha University of Science & Technology, Changsha, People's Republic of China.

China State Construction Hailong Technology, Shenzhen City, China.

出版信息

Sci Rep. 2023 Oct 28;13(1):18502. doi: 10.1038/s41598-023-45970-y.

DOI:10.1038/s41598-023-45970-y
PMID:37898683
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10613272/
Abstract

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.

摘要

众多关于项目调度的研究仅考虑单一因素,这无法反映项目运作的实际环境。鉴于此问题,本文综合多个视角,提出了基于多技能资源的多模式项目调度问题(MRCMPSP)。该问题通过资源能力矩阵及其他约束条件进行描述、建模和求解,以最小化项目工期。为有效解决MRCMPSP并丰富调度算法,本文基于量子粒子群算法(QPSO)选择了混合量子算法(HQPSO)。HQPSO引入了诸如JAYA优化搜索等各种改进措施来提升算法性能。为验证该算法的通用性、优越性和有效性,基于不同案例规模和资源数量设计了算法的独立运行比较实验和实际应用实验。实验结果表明,所提算法具有卓越的收敛性能和求解精度,能够为实际案例提供有效的调度解决方案。此外,本文还基于研究结果给出了针对性的管理建议。总体而言,本研究贡献了新颖的数学模型、求解算法、优化策略及管理见解,推动了项目管理研究领域的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/d983f1169295/41598_2023_45970_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/11dfa2697cef/41598_2023_45970_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/c3e3ec5a82c5/41598_2023_45970_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/b39b88f1d17d/41598_2023_45970_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/085c082272ad/41598_2023_45970_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/243338218263/41598_2023_45970_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/275f652eb683/41598_2023_45970_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/ab95d565754a/41598_2023_45970_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/d983f1169295/41598_2023_45970_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/11dfa2697cef/41598_2023_45970_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/c3e3ec5a82c5/41598_2023_45970_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/b39b88f1d17d/41598_2023_45970_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/085c082272ad/41598_2023_45970_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/243338218263/41598_2023_45970_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/275f652eb683/41598_2023_45970_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/ab95d565754a/41598_2023_45970_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/680a/10613272/d983f1169295/41598_2023_45970_Fig9_HTML.jpg

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本文引用的文献

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PLoS One. 2016 Mar 8;11(3):e0149589. doi: 10.1371/journal.pone.0149589. eCollection 2016.