Optimization-simulation modeling for resource allocation management in geographically distributed organizational systems with variable workloads

This paper addresses the integration of optimization approaches and simulation modeling to manage resource allocation within an organizational system characterized by a geographically distributed operational environment and variable activity volumes. The research methodology employs a systems approach, utilizing structural modeling to represent the organization's functioning and management. By structuring the interaction between the control center and operational units, the study establishes quantitative connection characteristics, which are recorded via the system's digital monitoring. The core component of this optimization-simulation model involves the multi-alternative selection of priority units for integrated resource allocation, subject to balance constraints and a stochastic flow of requests defining work requirements. Variable activity volumes are accounted for through a multi-period distribution of integrated resources. Consequently, the set of candidate units for the subsequent period includes those excluded from the optimized subset in the previous step, alongside a random component determined by the simulation results. The study demonstrates that single-period optimization utilizes real-time data to identify priority units for resource allocation. Furthermore, the multi-period optimization-simulation process generates sufficient synthetic data on resource demand; when combined with retrospective monitoring data, this forms a representative training dataset for machine learning predictive models. Finally, the paper defines management decisions supported by these predictive models for both the operational and developmental stages of the organizational system.

1. Novikov D.A. Theory of management of organizational systems. Moscow: LENAND; 2022. 500 p. (In Russ.).

2. Lvovich Ya.E., Lvovich I.Ya., Choporov O.N., et al. Optimization of digital management in organizational systems. Voronezh: Nauchnaya kniga; 2021. 191 p. (In Russ.).

3. Ivarovsky P.N. Analysis of the activities of construction and installation organizations. Brest: BrGTU; 2004. 195 p. (In Russ.).

4. Matveeva E.A., Novakovskaya T.S. Modelling of the organization of activity of the construction enterprise. Bulletin of the V.N. Tatishchev Volga State University. 2013;(4):35–40. (In Russ.).

5. Korchagin S.G., Ryndin A.A., Ryndin N.A. Management in organizational systems based on digital technologies. Voronezh: Nauchnaya kniga; 2025. 248 p. (In Russ.).

6. Donskoy V.I. Extraction of optimization models from data: an application of neural networks. Tauride Bulletin of Computer Science and Mathematics. 2018;(2):71–89. (In Russ.).

7. Snapelev Yu.M., Staroselsky V.A. Modeling and control in complex systems. Moscow: Sovetskoe radio; 1974. 264 p. (In Russ.).

8. Novoseltsev V.I. System analysis: modern concepts. Voronezh: Kvarta; 2003. 360 p. (In Russ.).

9. Sobol I.M. Numerical Monte Carlo methods. Moscow: Nauka; 1973. 312 p. (In Russ.).

10. Lvovich Ya.E. Multi-alternative optimization: theory and applications. Voronezh: Kvarta; 2006. 415 p. (In Russ.).

11. Kryuchin O.V., Kozadaev A.S., Dudakov V.P. Forecasting time series using artificial neural networks and regression models using the example of forecasting currency pair quotes. Researched in Russia. 2010;(30):354–362. (In Russ.).