Providing a robust model for air force transport fleet planning in military operations using a robust optimization approach

Document Type : Original Article

Authors

1 Ph.D., Department of Educational Industrial Engineering, Faculty of Industrial Engineering, Azad University, Parand branch

2 Associate Professor, Department of Industrial Engineering, Islamic Azad University, Parand Branch, Tehran, Iran

Abstract

In the framework of today's modern wars, the armed forces are facing many challenges and obstacles to achieve their military goals, which can affect and limit the power of predictions and battle planning in military operations. Therefore, in the current research, the aim is to design and present a mathematical model in a way that can enable the planning of the transportation fleet of the aerospace force with the highest efficiency and the lowest cost. Therefore, in this research, in order to find optimal options and formulate relationships between variables, one-stage linear programming method and entering samples through meta-heuristic algorithms and exploratory approaches in the state space close to the real environment have been used. Considering the conflicting goals in the atmosphere of ambiguity and uncertainty, the multi-objective optimization approach has been used in order to find a set of justified solutions in operational environments in order to redesign and organize the transport fleet; Therefore, after determining the assumptions, variables and parameters of the decision-making model, the robustness of the presented model against possible risks and limitations in different scenarios was investigated and evaluated through the genetic algorithm. The results of computational tests showed that heuristic algorithms, compared to classical methods, are a favorable solver for reaching optimal solutions for managing the costs of the transportation fleet of the Aerospace Force.

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References

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Defensive Researches and Management
Volume 22, Issue 99
Spring 2023
July 2023
Pages 173-199

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History

  • Receive Date: 22 August 2023
  • Revise Date: 06 December 2023
  • Accept Date: 22 April 2024

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