Mathematical model of budgeting distribution and allocation by using multi-objective particle swarm algorithm (case study: construction projects in Shirvan, Iran)

Articles in Press

Document Type : Research Paper

Authors
Arman Farahbakhsh
Asadollah Mahmoudzadeh Vaziri
Department of Mathematics, Faculty of Mathematical Science and Statistics, University of Birjand, Birjand, Iran.
10.22075/ijnaa.2024.35736.5313
Abstract
This study aims to present a budgeting model for organizational development projects that enables the efficient allocation and distribution of budgets across various sectors. A tri-objective budgeting model was developed and examined for implementation in Shirvan City. The proposed approach utilizes the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to optimize the model under investigation. The results of the proposed method indicate that employing the MOPSO algorithm leads to efficient budget allocation and distribution. In many organizations, resource allocation is still reliant on traditional and outdated methods, which often encounter significant challenges and inherent errors. Therefore, adopting a method that leverages mathematical models to determine optimal and effective allocation is of critical importance. Budgeting is recognized as an effective and efficient tool for achieving organizational objectives and resource allocation. This allocation process provides a system for controlling expenditures and costs.
Keywords
particle swarm optimization algorithm
multi-objective particle swarm optimization algorithm
budget
allocation and distribution
construction project
[1] A. Azadi, S. Delangizan, and A. Falahati, The role of governments in Iran regional development (Case study: Tenth government), Iran. Econ. Rev. 25 (2021), no. 1, 1-20.
[2] A. Azar, Mathematical modelling of cost planning in public organizations of Iran: Crisp and fuzzy approach, Knowledge of Manag. 35-36 (1996), 12-28. [In Persian]
[3] A. Azar and S. Najafi, Mathematical model of budgeting in the public sector: a robust optimization approach, Govern. Manag. Persp. 2 (2011), no. 4.
[4] J.C. Bansal, Particle swarm optimization, J. Bansal, P. Singh and N. Pal, (eds) Evolutionary and Swarm Intelligence Algorithm, Studies in Computational Intelligence, Springer, 2019.
[5] K.P. Bwire, Impact of operational budgeting on procurement performance: A case of manufacturing firms in the Jinja Industrial Hub, Doctoral dissertation, Makerere University, 2019.
[6] J.A. Chan and M. Pessoa, Finding the costs of public services: the experience of the state of São Paulo in implementing a cost system, M. Pessoa and C. Pimenta (eds.), Public financial management in latin america, Inter‑American Development Bank, 2015.
[7] A. Charnes and W. Cooper, Studies in Mathematical and Managerial Economics, North‑Holland Publishing Company, 1971.
[8] N. Fadda, L. Marinò, G. Pischedda, and A. Ezza, The effect of performance‑oriented funding in higher education: Evidence from the staff recruitment budget in Italian higher education, Higher Educ. 83 (2022), no. 5, 1003-1019.
[9] U.S. Igwe, S.F. Mohamed, and M.B.M.D. Azwarie, Recent technologies in construction; a novel search for total cost management of construction projects, IOP Conf. Ser.: Mater. Sci. Eng. 884 (2020), no. 1, 012041.
[10] J. Kennedy and R. Eberhart, Particle swarm optimization, In Proceedings of ICNN'95‑international conference on neural networks, IEEE 4 (1995), 1942-1948.
[11] A. Keykha, Thematic analysis components of performance‑based budgeting at universities, J. Public Admin. 13 (2021), no. 4, 706-737.
[12] Y. Marinakis, M. Marinaki, and A. Migdalas, A multi‑adaptive particle swarm optimization for the vehicle routing problem with time windows, Inf. Sci. 481 (2019), 311-329.
[13] D. Mohajan and H. Mohajan, Mathematical model for nonlinear budget constraint: Economic activities on increased budget, Stud. Soc. Sci. Human. 5 (2023), no. 2.
[14] M. Movahed and R. Rayanpour, Study of usage rate for various complementary and alternative medicine among people visited non‑conventional medical centers in Shiraz, Iran, in 2016, Bangladesh J. Med. Sci. 18 (2019), no. 2, 294.
[15] G.J. Privitera, Research methods for the behavioral sciences, Sage Publications, 2022.
[16] L. Seboni and J. Ssegawa, Does a project manager assignment process affect project management performance indicators? An empirical study, Sustainability 14 (2022), no. 13, 7637.
[17] H.K. Sowmya Shree and C.R. Shashi Kumar, A study on comparison of actual expenditure with budget at BSNL, Tumkur, Master of Business Administration Thesis, Visvesvaraya Technological University, Belagavi, 2019.
[18] L. Trigeorgis and A.E. Tsekrekos, Real options in operations research: A review, Eur. J. Oper. Res. 270 (2018), no. 1, 1-24.
[19] A. Wildavsky, The Politics of the Budgetary Process, Second Edition, Little, Brown, 1974.
[20] S.H. Zanakis, A multicriteria approach for library needs assessment and budget allocation, Socio‑Econ. Plann. Sci. 25 (1991), no. 3, 233-245.
International Journal of Nonlinear Analysis and Applications

Articles in Press, Corrected Proof
Available Online from 27 July 2026

  • Receive Date 25 October 2024
  • Revise Date 09 December 2024
  • Accept Date 11 December 2024