Просматривается номер   2026 / 01
For readers
About journal
Publication Ethics
Requirements for papers
Topics
Review Process
English (United Kingdom)Russian (CIS)
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
A. V. Sher, D. P. Matalov, V. V. Arlazarov Filtration of Local Features in the Problem of Creating Compact Document Templates
D. N. Gribanov Effect of Number and Complexity of Perspectives on 3D Reconstruction of an Object from a Single Image
Yu. S. Shevnina, A. A. Vinokurov, M. A. Petrova, K. S. Nikolaev, K. A. Kriushkin Forecasting the Change in the State of a Nonlinear System Using Cognitive Maps
V. V. Ryabov, V. A. Nemtinov, V. V. Alekseev Computer Model for Predicting Negative User Actions in Software Products with Network Effects
O. S. Makarov, E. V. Shchennikova Numerical Method for Recognizing Shadows of Moving Objects
A. D. Yakushev, A. M. Kashevnik Emotion Classification Method: Lightweight Neural Network with Attention
V. I. Baluta, V. P. Osipov, Yu. G. Rykov, B. N. Chetverushkin On Different Interpretations of Cognitive Map Technology in the Analysis of Complex Systems
MATHEMATICAL MODELLING
Y. S. Popkov Entropy Dimensionality Reduction of Data Matrix with Randomized (0,1)-Projectors
B. A. Chernysh, A. V. Murygin Optimization of Information Support for the Life Cycle of High)Tech Products at Early Stages
COMPUTING SYSTEMS AND NETWORKS
D. A. Grigoriev, N. A. Galanina Development of Diagnostic Methods for the RPA Terminal Software
Yu. A. Stepchenkov, Yu. G. Diachenko, N. V. Morozov, D. Yu. Diachenko Comparison of Practical Implementations of SelfTimed Cases of the Multiplier with Accumulation
E. S. Trushkin, V. I. Freyman Predictive Method of Resource Allocation in Computing Systems
MANAGEMENT AND DECISION MAKING
S. M. Beketov, M. V. Dergachev, A. M. Gintciak, P. A. Sharko Algorithms for Balancing Labor Resources in a Project Portfolio Based on a Multi-Agent Approach
M. P. Bazilevskiy The Implementation of the COPT Solver in Technology for Constructing Quite Interpretable Linear Regression Models
S. M. Beketov, M. V. Dergachev, A. M. Gintciak, P. A. Sharko Algorithms for Balancing Labor Resources in a Project Portfolio Based on a Multi-Agent Approach
Abstract.

The article solves the problem of balancing labor resources in the project portfolio using a multi-agent approach. Each project is modeled as an autonomous agent operating on the basis of the BDI paradigm, which allows its local limitations and preferences to be taken into account when making decisions. The main goal of agents is to minimize their own and total time and labor costs of the portfolio by redistributing resources. Algorithms for balancing resources are proposed, including evaluating the usefulness of resource exchange between agents, stochastic generation of project start times, and the construction of Pareto-optimal fronts based on time and labor criteria. Experiments on the data of five projects have shown that the proposed approach reduces the duration of the portfolio by 7.7%, and labor costs by 9.2%. The results demonstrate that a multi-agent approach to this task ensures lower portfolio implementation rates compared to centralized planning and balanced resource utilization.

Keywords: 

project portfolio management, multi-agent systems, BDI paradigm, resource balancing, Pareto optimization, stochastic generation.

DOI 10.14357/20718632260113

EDN UXXMRK

PP. 145-153.

References

1. Guo K., Zhang L. Multi-objective optimization for improved project management: Current status and future directions. Automation in Construction. 2022. Т. 139. рр. 104256.
2. Elkin S.A. Evaluation of software for project management in the field of IT: Problems and prospects. Ekonomika i upravlenie = Economics and Management. 2022;28(12):1219-1230. (In Russ.). http://doi.org/10.35854/1998-1627-2022-12-1219-1230 
3. Kerzner H. Project management: a systems approach to planning, scheduling, and controlling. John Wiley & Sons, 2025. 
4. Beketov S. M., Dergachev M. V., Gintciak A. M., Redko S. G. Team Formation in Software Projects: Multi-criteria Pareto Optimization. Programmnaya Ingeneria. 2025. Vol. 16, No. 2. рр. 92-99. http://doi.org/10.17587/prin.16.92-99.
5. Beketov, S. M. A human capital simulation model in innovation projects. Control Sciences. 2024. № 3. рр. 16-25. http://doi.org/10.25728/cs.2024.3.2.
6. Reiff J., Schlegel D. Hybrid project management–a systematic literature review. International journal of information systems and project management. 2022. Т. 10. №. 2. рр. 45-63. 
7. Muradyan I.V., Grigorenko O.V. Evaluating the effectiveness of it projects under conditions of uncertainty. Bulletin of the Academy of Knowledge. 2024. №. 4 (63). рр. 289-295. 
8. Larichev O.I. The science and Art of decision making. Pub.: Nauka. 1979. 199 р. (In Russ.). 
9. Zuev Yu.A., Larichev O.I., Filippov V.A., Chuev Yu.V. Problems of evaluating proposals for scientific research. Bulletin of the USSR Academy of Sciences. 1979. № 8, рр. 29-39. (In Russ.).
10. Roizenzon G.V. The synergetic effect in decision-making. System research. Methodological problems. The Yearbook. Edited by Yu. S. Popkov, V. N. Sadovsky, V. I. Tishchenko. № 36. 2012. рр. 248-272. (In Russ.). 
11. Kozhukharov А.N., Larichev О.I. A multi-criterial assignment problem. Automation and telemechanics. 1977. № 38(7) рр. 71-87. (In Russ.). 
12. Larichev O.I., Sternin M.Yu. Human-machine methods for solving a multi-criteria assignment problem. Automation and telemechanics. 1998. № 59(7). рр. 135-156. (In Russ.). 
13. Mousa M., van de Berg D., Kotecha N., del Rio Chanona E. A., Mowbray M. An analysis of multi-agent reinforcement learning for decentralized inventory control systems. Computers & Chemical Engineering. 2024. 188. 108783.
14. Pospelov D.A. Multi-agent systems–present and future. Information technologies and computing systems. 1998. № 1. рр. 14-21. (In Russ.). 
15. Slovokhotov Yu.L., Novikov D.A. Distributed intelligence of multi-agent systems. Part 1. Main characteristics and simplest forms. Control Sciences. 2023. № 5. pp. 3-22. (In Russ.).
16. Smirnov A.V., Sheremetov L. B. Models of forming coalitions of cooperative agents: the state and prospects of research. Artificial intelligence and decision-making. 2011. № 1, pp. 36-48. (In Russ.).
17. Rao A. S., Georgeff M. P. BDI agents: from theory to practice. In Icmas. 1995. Vol. 95, pp. 312-319. 
18. Mahfooz Ul Haque H., Saleem K., Salman Khan A. Modeling belief‐desire‐intention reasoning agents for situation‐aware formalisms. Concurrency and Computation: Practice and Experience. 2023. Т. 35. №. 15. С. e6417.
19. Goonatilleke S. T., Hettige B. Past, Present and Future Trends in Multi-Agent System Technology. Journal Européen des Systèmes Automatisés. 2022. Т. 55. №. 6. рр. 723-739. 
20. Kamel A., Elwageeh M., Bonduà S., Elkarmoty M. Evaluation of mining projects subjected to economic uncertainties using the Monte Carlo simulation and the binomial tree method: Case study in a phosphate mine in Egypt. Resources Policy. 2023. 80. 103266.
21. Senova A., Tobisova A., Rozenberg R. New approaches to project risk assessment utilizing the Monte Carlo method. Sustainability. 2023. Т. 15. №. 2. С. 1006.
22. Beketov S. M., Gintciak A. M., Dergachev M. V. Algorithm for estimating the convergence of stochastic Pareto optimization. Journal of information technologies and computing systems. 2024. (4). рр. 91-99. (In Russ.). 
23. Petchrompo S., Coit D. W., Brintrup A., Wannakrairot A., Parlikad A. K. A review of Pareto pruning methods for multi-objective optimization. Computers & Industrial Engineering. 2022. 167. 108022. 
24. Lotov A.V., Bushenkov V.A., Kamenev G.K., Chernykh O.L. The computer and the search for a compromise. The method of achievable goals. Pub.: Nauka. 1997. 239 p. (In Russ.). 
25. Jafarzadeh H., Heidary-Dahooie J., Akbari P., Qorbani A. A project prioritization approach considering uncertainty, reliability, criteria prioritization, and robustness. Decision Support Systems. 2022. 156. 113731.
26. Trenerry B., Chng S., Wang Y., Suhaila Z. S., Lim S. S., Lu H. Y., Oh P. H. Preparing workplaces for digital transformation: An integrative review and framework of multilevel factors. Frontiers in psychology. 2021. 12. 620766.
27. Allahham M. S., Abdellatif A. A., Mhaisen N., Mohamed A., Erbad A., Guizani M. Multi-agent reinforcement learning for network selection and resource allocation in heterogeneous multi-RAT networks. IEEE Transactions on Cognitive Communications and Networking. 2022. 8(2). 1287-1300.
28. Nagarajan S., Rani P. S., Vinmathi M. S., Subba Reddy V., Saleth A. L. M., Abdus Subhahan D. Multi agent deep reinforcement learning for resource allocation in container‐based clouds environments. Expert Systems. 2025. 42(1). e13362
     
2026 / 01
2025 / 04
2025 / 03
2025 / 02

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".