A review and comparison of newer methods for task allocation among performers

Timofey Yakovlevich  Shevgunov; Anna Alexandrovna  Kroshilina

doi:10.17323/2587-814X.2026.1.67.85

Timofey Yakovlevich Shevgunov Moscow Aviation Institute (National Research University), Moscow, Russia; Graduate School of Business, HSE University, Moscow, Russia https://orcid.org/0000-0003-1444-983X
Anna Alexandrovna Kroshilina Graduate School of Business, HSE University, Moscow, Russia

DOI: https://doi.org/10.17323/2587-814X.2026.1.67.85

Keywords: task distribution, task allocation, performer assignment, assignment matrix, task delegation algorithms, round-robin algorithm, front based algorithm, genetic algorithm management, product digital twin, resource digital twin

Abstract

This paper presents a description of the current state and the results of an analysis of recent advances in the problem domain of automated task distribution among employees. The purpose of the study is to identify the main trends and patterns in the development of existing task allocation methods, to determine their strengths and limitations, and to justify the need for new approaches and algorithms that can improve the efficiency of task delegation to employees. Using a unified system of notations for the key concepts of the subject area, the article provides a concise descriptive review of ten universal task distribution algorithms published over the past twenty years. The comparative analysis was carried out according to a set of criteria reflecting both the technical and the organizational-behavioral aspects of how these algorithms function. The key evaluation criteria included: the degree to which performer competencies are taken into account; adaptability to changing external conditions and team composition; requirements for completeness and structure of the input data; robustness to incomplete or noisy data; transparency and explainability of decision-making; computational complexity; scalability with an increasing number of tasks and employees; implementation and maintenance costs; and orientation toward personnel development and competence enhancement. The comparative analysis we carried out made it possible to identify the advantages and shortcomings of each method and to formulate recommendations for their most effective practical application. The results showed that none of the examined algorithms can be considered a universal tool for delegation. Furthermore, it was found that comprehensive information about a performer’s suitability for solving tasks requiring diverse competencies is either ignored or insufficiently utilized by many algorithms. This observation leaves open the problem of developing new approaches to task allocation and designing new algorithms based on them.

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References

Baldin, K. V., Perederyaev, I. I., & Golov, R. S. (2017). Upravlenie riskami v innovatsionno-investitsionnoi deyatel'nosti [Risk management in innovation and investment activities]. Moscow: Dashkov i K (in Russian).

Chauhan, N., Kaur, N., Saini, K. S., Verma, S., Alabdulatif, A., Khurma, R. A., Garcia-Arenas, M., & Castillo, P. A. (2024). A systematic literature review on task allocation and performance management techniques in cloud data center. Computer Systems Science and Engineering, 48(3), 571–608. https://doi.org/10.32604/csse.2024.042690

Zhang, P., Zhang, A., & Xu, G. (2020). Optimized task distribution based on task requirements and time delay in edge computing environments. Engineering Applications of Artificial Intelligence, 94, 103774. https://doi.org/10.1016/j.engappai.2020.103774

Al-Fraihat, D., Sharrab, Y., Al-Ghuwairi, A.-R., Alzabut, H., Beshara, M., & Algarni, A. (2024). Utilizing machine learning algorithms for task allocation in distributed agile software development. Heliyon, 10(21), e39926. https://doi.org/10.1016/j.heliyon.2024.e39926

Skaltsis, G. M., Shin, H.-S., & Tsourdos, A. (2023). A review of task allocation methods for UAVs. Journal of Intelligent & Robotic Systems, 109(4), 76. https://doi.org/10.1007/s10846-023-02011-0

Lerner, I. M., Marinosyan, A. Kh., Grigoriev, S. G., Yusupov, A. R., Anikyeva, M. A., & Garifullina, G. A. (2024). An approach to the formation of intellectual academic genealogy using large language models. Electromagnetic Waves and Electronic Systems, 29(4), 108–120 (in Russian). https://doi.org/10.18127/j5604128-202404-09

Grigoriev, S. G., Lerner, I. M., Marinosyan, A. Kh., Arutyunova, N. K., & Grigorieva, M. A. (2025). On the issue of educational and methodological information selection for implementing an adaptive learning management system: Algorithm of a priori authors classification. Informatics and Education, 40(2), 66–78 (in Russian). https://doi.org/10.32517/0234-0453-2025-40-2-66-78

Grigoriev, S. G., Lerner, I. M., Marinosyan, A. Kh., & Grigorieva, M. A. (2025). On the issue of educational and methodological information selection for implementing an adaptive learning management system: Algorithm of selecting authors of literature taking into account the emotional and psychological characteristics of users based on the ideas of academic genealogy. Informatics and Education, 40(3), 69–79 (in Russian). https://doi.org/10.32517/0234-0453-2025-40-3-69-79

Beklaryan, A. L., & Akopov, A. S. (2018). Simulation model of the optimal allocation credit applications for interregional underwriting center of a commercial bank. Vestnik Komp’iuternykh i Informatsionnykh Tekhnologii, 11, 46–56 (in Russian). https://doi.org/10.14489/vkit.2018.11.pp.046-056

Taranenko, D. P., & Kolesnikov, A. V. (2021). Features of delegation of powers in the context of the development of remote control technologies. Normirovanie i Oplata Truda v Promyshlennosti (Rationing and Remuneration of Labor in Industry), 12, 56–58 (in Russian). https://doi.org/10.33920/pro-3-2112-06

Schaad A., & Pyrmont B. (2010). Review mechanism for controlling the delegation of tasks in a workflow system. US Patent No. 7831978.

Akhavein, J., Frame, W. S., & White, L. J. (2005). The diffusion of financial innovations: An examination of the adoption of small business credit scoring by large banking organizations. The Journal of Business, 78(2), 577–596. https://doi.org/10.1086/427639

Gupta, D., & Denton, B. (2008). Appointment scheduling in health care: Challenges and opportunities. IIE Transactions, 40(9), 800–819. https://doi.org/10.1080/07408170802165880

Abanda, F. H., Musa, A. M., Clermont, P., Tah, J. H. M., & Oti, A. H. (2020). A BIM-based framework for construction project scheduling risk management. International Journal of Computer Aided Engineering and Technology, 12(2), 182. https://doi.org/10.1504/ijcaet.2020.105575

Voigt, S., & El Bialy, N. (2013). Identifying the determinants of judicial performance: Taxpayers’ money well spent? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2241224

Aksin, Z., Armony, M., & Mehrotra, V. (2007). The modern call center: A multi‐disciplinary perspective on operations management research. Production and Operations Management, 16(6), 665–688. https://doi.org/10.1111/j.1937-5956.2007.tb00288.x

Yadav, P.K., Saxena, S. (2020). Design and implementation of Round Robin scheduling algorithm using dispatch latency. International Online Conference on Emerging Trends in Multi-Disciplinary Research (ETMDR-2020), 356–365.

Eremina, I. I., & Lysanov, D. M. (2020). Matematicheskaya model’ optimizatsii protsessa raspredeleniya zadach i trudovykh resursov na predpriyatii [Mathematical Model for Optimizing the Process of Task and Labor Resource Distribution in Enterprises]. American Scientific Journal, 42, 65–71 (in Russian).

Korchevskaya, E. A., Ermachenko, S. A., Nikonova, T. V., Markova, L. V., & Shpakova, Y. A. (2023). Ispol’zovanie geneticheskogo algoritma dlya resheniya zadachi raspredeleniya uchebnoi nagruzki [Using a genetic algorithm to solve the problem of distributing academic workload]. Vesnik Vitsebskogo gosudarstvennogo universiteta, 3(120), 15–19 (in Russian).

Akopov, A. S., & Hevencev, M. A. (2013). A Multi-agent Genetic Algorithm for Multi-objective Optimization. 2013 IEEE International Conference on Systems, Man, and Cybernetics, 1391–1395. https://doi.org/10.1109/smc.2013.240

Munerman, V. I., & Munerman, D. V. (2019). Analysis of an optimal distribution algorithm. Modern Information Technologies and IT-Education, 15(3), 619–625 (in Russian). https://doi.org/10.25559/sitito.15.201903.619-625

Suresh, M., Samuel, R. B., Bhuvaneswar, T., Jaubin, R. H., & Balaji, R. (2020). Automation of employee workload management using random sample partition algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 5282–5286. https://doi.org/10.35940/ijrte.f9761.038620

Salloum, S., Huang, J. Z., & He, Y. (2019). Random sample partition: A distributed data model for big data analysis. IEEE Transactions on Industrial Informatics, 15(11), 5846–5854. https://doi.org/10.1109/tii.2019.2912723

Luk’yanov, L. A., Spivak, S. I., & Khristolyubov, V. L. (2016). Neironnaya set’ korrektor dlya raspredeleniya rabot v zadache vnutritsekhovogo planirovaniya [Neural network corrector for job distribution in intra-shop scheduling]. Vestnik Bashkirskogo universiteta, 21(4), 859–863 (in Russian).

Efimov, E. N., & Shevgunov, T. Ya. (2012). Development and analysis of the technique for the building of artificial neural networks based on adaptive elements. Trudy MAI, 51 (in Russian). https://trudymai.ru/eng/published.php?ID=29159

Kokhonen T. (2017). Samoorganizuyushchiesya karty [Self-Organizing Maps]. Moscow: Laboratoriya znanii (in Russian).

Novikova, T., & Novikov, A. (2015). Algorithms for solving problems of optimum distribution work in network canonical structures. Forestry Engineering Journal, 4(4), 309–317 (in Russian). https://doi.org/10.12737/8515

Pop, B., & Boian, F. (2014). Algorithms for automating task delegation in project management. Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, 2, 1191–1196. https://doi.org/10.15439/2014f426

Lopes, A. L., & Botelho, L. M. (2007). Task decomposition and delegation algorithms for coordinating unstructured multi agent systems. First International Conference on Complex, Intelligent and Software Intensive Systems (CISIS’07), 209–214. https://doi.org/10.1109/cisis.2007.52

Wang, S., Li, Q., Cui, L., Yan, Z., Xu, Y., Shi, Z., Min, X., Shen, Z., & Yu, H. (2022). Towards AI-empowered crowdsourcing. arXiv:2212.14676. https://doi.org/10.48550/arXiv.2212.14676

Al-Anzi, F. S., Al-Zame, K., & Allahverdi, A. (2010). Weighted multi-skill resources project scheduling. Journal of Software Engineering and Applications, 03(12), 1125–1130. https://doi.org/10.4236/jsea.2010.312131