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Computer Research and Modeling, 2019, Volume 11, Issue 2, Pages 343–358
DOI: https://doi.org/10.20537/2076-7633-2019-11-2-343-358
(Mi crm715)
 

This article is cited in 14 scientific papers (total in 14 papers)

MODELS OF ECONOMIC AND SOCIAL SYSTEMS

Model for operational optimal control of financial recourses distribution in a company

E. V. Orlova

Ufa state aviation technical university, 12 K. Marks st., Ufa, 450000, Russia
References:
Abstract: A critical analysis of existing approaches, methods and models to solve the problem of financial resources operational management has been carried out in the article. A number of significant shortcomings of the presented models were identified, limiting the scope of their effective usage. There are a static nature of the models, probabilistic nature of financial flows are not taken into account, daily amounts of receivables and payables that significantly affect the solvency and liquidity of the company are not identified. This necessitates the development of a new model that reflects the essential properties of the planning financial flows system — stochasticity, dynamism, non-stationarity.
The model for the financial flows distribution has been developed. It bases on the principles of optimal dynamic control and provides financial resources planning ensuring an adequate level of liquidity and solvency of a company and concern initial data uncertainty. The algorithm for designing the objective cash balance, based on principles of a companies’ financial stability ensuring under changing financial constraints, is proposed.
Characteristic of the proposed model is the presentation of the cash distribution process in the form of a discrete dynamic process, for which a plan for financial resources allocation is determined, ensuring the extremum of an optimality criterion. Designing of such plan is based on the coordination of payments (cash expenses) with the cash receipts. This approach allows to synthesize different plans that differ in combinations of financial outflows, and then to select the best one according to a given criterion. The minimum total costs associated with the payment of fines for non-timely financing of expenses were taken as the optimality criterion. Restrictions in the model are the requirement to ensure the minimum allowable cash balances for the subperiods of the planning period, as well as the obligation to make payments during the planning period, taking into account the maturity of these payments. The suggested model with a high degree of efficiency allows to solve the problem of financial resources distribution under uncertainty over time and receipts, coordination of funds inflows and outflows. The practical significance of the research is in developed model application, allowing to improve the financial planning quality, to increase the management efficiency and operational efficiency of a company.
Keywords: operational financial plan, financial flows, coordinated management, discrete optimal control, dynamic programming method, risk minimization.
Received: 08.12.2018
Revised: 23.12.2018
Accepted: 18.01.2019
Document Type: Article
UDC: 519.777.5; 330.46
Language: Russian
Citation: E. V. Orlova, “Model for operational optimal control of financial recourses distribution in a company”, Computer Research and Modeling, 11:2 (2019), 343–358
Citation in format AMSBIB
\Bibitem{Orl19}
\by E.~V.~Orlova
\paper Model for operational optimal control of financial recourses distribution in a company
\jour Computer Research and Modeling
\yr 2019
\vol 11
\issue 2
\pages 343--358
\mathnet{http://mi.mathnet.ru/crm715}
\crossref{https://doi.org/10.20537/2076-7633-2019-11-2-343-358}
Linking options:
  • https://www.mathnet.ru/eng/crm715
  • https://www.mathnet.ru/eng/crm/v11/i2/p343
  • This publication is cited in the following 14 articles:
    1. Hamideh Bahrami, Saeed Yaghoubi, “Dynamic optimal pricing considering advertising and improvement quality strategies over product life-cycle horizon”, Flex Serv Manuf J, 2024  crossref
    2. Armando Nieto, Marti Serra, Angel A. Juan, Christopher Bayliss, “A GA-simheuristic for the stochastic and multi-period portfolio optimisation problem with liabilities”, Journal of Simulation, 17:5 (2023), 632  crossref
    3. Ekaterina V. Orlova, 2023 5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 2023, 387  crossref
    4. D. Umamaheswari, P. Ramesh, “A study in analysing the role of labour enactment towards employment protection and labour productivity”, ijhs, 2022, 3213  crossref
    5. Ekaterina V. Orlova, 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 2021, 161  crossref
    6. Ekaterina Orlova, “Innovation in Company Labor Productivity Management: Data Science Methods Application”, ASI, 4:3 (2021), 68  crossref
    7. Ekaterina V. Orlova, “Design of Personal Trajectories for Employees' Professional Development in the Knowledge Society under Industry 5.0”, Social Sciences, 10:11 (2021), 427  crossref
    8. Ekaterina V. Orlova, “Methodology and Models for Individuals' Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods”, Mathematics, 9:15 (2021), 1820  crossref
    9. Ekaterina V. Orlova, 2021 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 2021, 202  crossref
    10. Ekaterina V. Orlova, “Decision-Making Techniques for Credit Resource Management Using Machine Learning and Optimization”, Information, 11:3 (2020), 144  crossref
    11. E. V. Orlova, 2020 2nd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), 2020, 705  crossref
    12. E. V. Orlova, Lecture Notes in Mechanical Engineering, Proceedings of the 5th International Conference on Industrial Engineering (ICIE 2019), 2020, 427  crossref
    13. E. V. Kasatkina, D. D. Vavilova, “Mathematical Model of Optimal Development Enterprise Economics”, Statistika i ekonominka, 17:2 (2020), 72  crossref
    14. Christopher Bayliss, Marti Serra, Armando Nieto, Angel A. Juan, “Combining a Matheuristic with Simulation for Risk Management of Stochastic Assets and Liabilities”, Risks, 8:4 (2020), 131  crossref
    Citing articles in Google Scholar: Russian citations, English citations
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