EMPLOYING NETWORK OPTIMIZATION TECHNIQUES FOR RESOURCE DISTRIBUTION IN MILITARY SPHERE AMIDST A SURPLUS OF HOMOGENEOUS ASSETS
DOI:
https://doi.org/10.17721/2519-481X/2024/85-08Keywords:
linear programming, network optimization, transportation problem, resource allocation, combat efficiencyAbstract
The paper addresses the problem of optimal resource allocation in military conditions, which has arisen due to the diversity of technical assistance, the specifics of modern equipment operation, and significant time and material constraints in personnel training. The primary goal of this research is the mathematical formalization of the problem, aimed at solving the outlined issue, and an examination of its solution using well-established mathematical methods of operations research. This approach maximizes the efficiency of human and material resources, including during the development and implementation of the proposed methodology.
The first section of the study briefly outlines the fundamental concepts of network optimization theory, including maximum flow and minimum cost problems. The feasibility of applying these methods to resource allocation tasks, considering constraints and affinities between resources and users, is demonstrated.
In the main part of the paper, a specific example of a weapon allocation task among military personnel is presented. The mathematical model of the task, including its graphical representation, is formulated, and the solution procedure is demonstrated. The Vogel approximation method is used to determine an initial feasible solution, followed by the potentials method to achieve an optimal allocation. The results demonstrate the potential to maximize the utilization of personnel skills, which is a key element in enhancing the efficiency of the respective unitapos;s operations.
The concluding section highlights the prospects of automating the resource allocation process using the proposed algorithms. The applicability of these methods in civilian fields, such as logistics and personnel management, is substantiated, along with directions for further research, including incorporating additional optimization criteria and assessing the impact of input data uncertainty on the results.
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