{"id":6717,"date":"2026-02-22T22:17:40","date_gmt":"2026-02-22T22:17:40","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=6717"},"modified":"2026-03-11T10:41:28","modified_gmt":"2026-03-11T10:41:28","slug":"28-1-12-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-1-12-en\/","title":{"rendered":"28.1.12 En"},"content":{"rendered":"\n

Fuzzy metaheuristic method for allocating employees to project tasks<\/strong><\/h1>\n\n\n\n

D.D. Yartsev, M.Yu. Vorotilova, S.V. Mordvin<\/strong><\/p>\n\n\n\n

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Upload the full text<\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong>:<\/strong> <\/em>Project implementation planning plays a key role in project management, especially in the context of rapidly increasing project complexity and resource constraints.
Aim<\/strong>. This study was to develop a method for optimally distributing project staff across tasks, minimizing staff overload and ensuring adherence to time and budget constraints.
Materials and methods<\/strong>. To solve this problem, we proposed a hybrid method combining the Worm Optimization algorithm, which models the foraging behavior of Caenorhabditis elegans worms, and the fuzzy clustering method FCM, which is used to narrow the solution space. The Python libraries NetworkX, Scikit-Fuzzy, Matplotlib, and Plotly were used to implement the proposed method. To reduce the search space dimensionality, fuzzy clustering FCM was used, enabling the identification of a solution cluster characterizing the most suitable employees in terms of team characteristics.
Results<\/strong>. As part of the study, a software tool in Python has been implemented that enables automated task distribution among performers. A computational experiment has also been conducted to evaluate the effectiveness of the proposed method and compare its results with those of the popular bioinspired Ant Colony Optimization algorithm, which is characterized by similar agent behavior patterns during solution search.
Conclusions<\/strong>. The validation results demonstrate the advantage of the hybrid method over the basic Worm Optimization algorithm and Ant Colony Optimization, as evidenced by lower objective function values and reduced employee overload, confirming the method’s effectiveness in assigning project performers to tasks.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> human resource management, conditional multicriteria optimization, metaheuristics, \u201cWorm Optimization\u201d, fuzzy clustering<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Yartsev D.D., Vorotilova M.Yu., Mordvin S.V. Fuzzy metaheuristic method for allocating employees to project tasks. News of the Kabardino-Balkarian Scientific Center of RAS. 2026. Vol. 28. No. 1. Pp. 175\u2013187. DOI: 10.35330\/1991-6639-2026-28-1-175-187<\/p>\n\n\n\n

\u00a9   Yartsev D.D., Vorotilova M.Yu., Mordvin S.V., 2026<\/p>\n\n\n\n

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Content is available under license Creative Commons Attribution 4.0 License<\/a><\/p>\n<\/div>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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    Information about the author<\/strong>s<\/summary>\n
    \n

    Denis D. Yertsev<\/strong>, Postgraduate Student, Rosinformagrotech;
    60, Lesnaya street, Pushkinsky Urban District, Pravdinsky Urban Settlement, Moscow Region, 141261, Russia;
    Yarcevdd@me.com, ORCID: https:\/\/orcid.org\/0009-0009-0760-4783
    Margarita Yu. Vorotilova<\/strong>, Postgraduate Student, Branch of the National Research University \u201cMoscow Power Engineering Institute\u201d in Smolensk;
    1, Energetichesky proezd, Smolensk, 214013, Russia;
    rita.vorotilova@mail.ru, ORCID: https:\/\/orcid.org\/0009-0001-4646-3540, SPIN-code: 6572-7685
    Sergey V. Mordvin<\/strong>, Assistant of the Department, Branch of the National Research University \u201cMoscow Power Engineering Institute\u201d in Smolensk;
    1, Energetichesky proezd, Smolensk, 214013, Russia;
    mors@1eska.ru, ORCID: https:\/\/orcid.org\/0009-0005-8121-0716<\/p>\n\n\n\n

    <\/p>\n<\/div>\n<\/details>\n\n\n\n

    Funding<\/strong><\/summary>\n

    The study was performed without external funding.<\/p>\n<\/details>\n","protected":false},"excerpt":{"rendered":"

    Fuzzy metaheuristic method for allocating employees to project tasks D.D. Yartsev, M.Yu. Vorotilova, S.V. Mordvin Upload the full text Abstract: Project implementation planning plays a key role in project management, especially in the context of rapidly increasing project complexity and resource constraints.Aim. This study was to develop a method for optimally distributing project staff across […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-6717","page","type-page","status-publish","hentry"],"yoast_head":"\n28.1.12 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/28-1-12-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"28.1.12 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Fuzzy metaheuristic method for allocating employees to project tasks D.D. 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Yartsev, M.Yu. Vorotilova, S.V. Mordvin Upload the full text Abstract: Project implementation planning plays a key role in project management, especially in the context of rapidly increasing project complexity and resource constraints.Aim. 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