Global Journal of Economic and Finance Research

Financing decision-making is one of the main challenges facing organizations in a complex and multi alternative economic environment. In this context, this research ought to improve funding decisions by employing the linear programming model, in particular the transport model, supported by the simulation of linear weighting, in the distribution of financing resources within OCP to four strategic projects. The research aims to provide a quantitative model that supports decision-making in the absence of accurate data on demand, with a focus on achieving a balance Between the strategic priorities and financial constraints associated with the various sources of financing, represented in international bonds, strategic partnerships and international loans. The methodology relied on building a mathematical model that represents the funding relationships between projects and sources, and used linear weight simulation to determine the relative importance of each project, then the data was processed and analyzed using Python programming tools. The results showed that the proposed model contributed to providing an effective and objective funding distribution that reflects the relative importance of projects without exceeding the imposed restrictions, and also allowed flexibility in testing multiple scenarios and possible data updates. The added value of this model is highlighted in its ability to support decisions Finance in environments of uncertainty and a multitude of alternatives, providing a quantitative tool applicable at the level of strategic institutions. Thus, the research concludes that the integration of linear programming with linear weighting simulation constitutes a practical framework for improving the efficiency of resource allocation and enhancing the quality of financial decision in contemporary institutions.

linear programming, transfer problems, linear weighting simulation of weight distribution, institutional finance

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