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salehi, P., khalaj, M., jafari, D. (2023). Providing a robust model for air force transport fleet planning in military operations using a robust optimization approach. Electronic and Cyber Defense, 22(99), 173-199.
pejman salehi; mehran khalaj; davoud jafari. "Providing a robust model for air force transport fleet planning in military operations using a robust optimization approach". Electronic and Cyber Defense, 22, 99, 2023, 173-199.
salehi, P., khalaj, M., jafari, D. (2023). 'Providing a robust model for air force transport fleet planning in military operations using a robust optimization approach', Electronic and Cyber Defense, 22(99), pp. 173-199.
salehi, P., khalaj, M., jafari, D. Providing a robust model for air force transport fleet planning in military operations using a robust optimization approach. Electronic and Cyber Defense, 2023; 22(99): 173-199.

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

مدیریت و پژوهش‌های دفاعی
Volume 22, Issue 99, July 2023, Pages 173-199    PDF (1.02 M)
Document Type: Original Article
Authors
pejman salehi* 1; mehran khalaj2; davoud jafari2
1Ph.D., Department of Educational Industrial Engineering, Faculty of Industrial Engineering, Azad University, Parand branch
2Associate Professor, Department of Industrial Engineering, Islamic Azad University, Parand Branch, Tehran, Iran
Receive Date: 22 August 2023,  Revise Date: 06 December 2023,  Accept Date: 22 April 2024 
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.
Keywords
robustness; military transport fleet; military operation; air force; genetic algorithm
References
خاندوزی، راحله. (1399): یک روش جدید برای حل مسئله مکان‌یابی تجهیزات نظامی برای حفاظت سایت‌های استراتژیک با استفاده از الگوریتم فراابتکاری آموزش و یادگیری. دوفصلنامه بازی جنگ، دوره 3، شماره 6، صص 155 تا صفحه 193.

رجبی مشتاقی، حجت اله. طلوعی اشلقی، عباس. معتدل، محمدرضا. (1400): ارائه یک الگوریتم فراابتکاری جدید: الگوریتم بهینه‌سازی نظامی، شماره 3، دوره 6، صص304-329.

شهلائی، ناصر. مرادیان، محسن. لطفی، احمد. هادی‌نژاد، فرهاد. (1395): طراحی مدل ریاضی برای پیش‌بینی مکان بهینه مراکز آمادی در شرایط جنگ‌های آینده. فصلنامه آینده‌پژوهی دفاعی. دوره 1، شماره 1، شهریور 1395، صص 45-64.

قربانی صابر، رضا. رنجبر، محمد . (1397). بهینه‌سازی و حل مسئله تخصیص و زمان‌بندی سنسور - سلاح/تهدید به‌صورت یکپارچه. چهارمین کنفرانس بین‌المللی مهندسی صنایع و سیستم‌ها

مددی، سعید. تومانیان، آرا. اخوان، امیرناصر. (1397): سناریوهای پیش‌روی شاخص‌های راهبردی توسعه فناوری اطلاعات مکانی در حوزه دفاعی با رویکرد پویایی سیستم، فصلنامه آینده‌پژوهی دفاعی، دوره 3، شماره 11. صص 119-142.

Abbass, H. A., Bender, A., Dam, H. H., Baker, S., Whitacre, J., & Sarker, R. (2018). Computational scenario-based capability planning. In Proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 1437-1444).

Abbass, H., Bender, A., Baker, S., & Sarker, R. (2017). Anticipating future scenarios for the design of modularised vehicle and trailer fleets. In SimTecT2007, Simulation Conference.

Adamides, E., Stamboulis, Y., & Varelis, A. 2014. Model-based assessment of military aircraft engine maintenance systems. Journal of the Operational Research Society, 55(9), 957–967.

AM Grisogono and A Ryan, Designing complex adaptive systems for defence, Proc. SETE Conference, Canberra, Australia, 2003.

Ausseil, R., Gedik, R., Bednar, A., & Cowan, M. (2020). Identifying sufficient deception in military logistics. Expert Systems with Applications, 141, Article 112974.

Baker, S. F., Morton, D. P., Rosenthal, R. E., & Williams, L. M. (2002). Optimizing military airlift. Operations Research, 50(4), 582–602.

Bankes, S. C. (2012). Agent-based modeling: A revolution?. Proceedings of the National Academy of Sciences, 99(suppl_3), 7199-7200.

Bar-Yam, Y. (2013). Dynamics of complex systems (Studies in Nonlinearity). Boulder.

Baykasoğlu, A., Subulan, K., Taşan, A. S., & Dudaklı, N. (2019). A review of fleet planning problems in single and multimodal transportation systems. Transportmetrica A: Transport Science, 15(2), 631–697.

Bisht, S. 2014. Hybrid genetic-simulated annealing algorithm for optimal weapon allocation in multilayer defence scenario. Defence Science Journal, 54(3), 395.

Bivona, E., & Montemaggiore, G. B. (2015). Understanding short-and long-term implications of ‘‘myopic’’ fleet maintenance policies: A system dynamics application to a city bus company. System Dynamics Review, 26(3), 195–215.

Blank, J., & Deb, K. (2022). pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. arXiv preprint arXiv:2204.05855.

Brown, G. G., & Kline, J. E. (2021). Optimizing navy mission planning. Military Operations Research, 26(2), 39–58,

Deng, Q., Santos, B. F., & Verhagen, W. J. (2021). A novel decision support system for optimizing aircraft maintenance check schedule and task allocation. Decision Support Systems, 146, Article 113545.

Emerson, D. E. 2003. Simulation models for assessing force generation and logistics support in a combat environment. Systems Analyses and Modelling in Defence, Development Trends and Issues.

Guliev, I., Kerimov, V. Y., Etirmishli, G., Yusubov, N., Mustaev, R., & Huseynova, A. (2021). Modern geodynamic processes and their impact on replenishment of hydrocarbon resources in the black sea–caspian region. Geotectonics, 55(3), 393–407.

Hellyer, M. (2020). The cost of defence 2020–2021. Part 2: ASPI defence budget brief | Australian Strategic Policy Institute | ASPI.

Lausch, A., & Wesolkowski, S. (2018). Matching air mobility tasks to platforms: Preliminary algorithm and results. DRDC CORA Technical Note.(under review).

Leboucher, C., Shin, H. S., Le Ménec, S., Tsourdos, A., Kotenkoff, A., Siarry, P., & Chelouah, R. 2017. Novel evolutionary game based multi-objective optimisation for dynamic weapon target assignment. IFAC Proceedings Volumes, 47(3), 3936-3941.

Li, J., Ge, B., Jiang, J., Yang, K., & Chen, Y. 2023. High-end weapon equipment portfolio selection based on a heterogeneous network model. Journal of Global Optimization, 78, 743-761.

Li, X., & Epureanu, B. I. (2020). An agent-based approach to optimizing modular vehicle fleet operation. International Journal of Production Economics, 228, Article 107733.

Mazurek, M., & Wesolkowski, S. (2012). Fleet mix computation using evolutionary multiobjective optimization. In 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM) (pp. 46-50). IEEE.

Rempel, M. (2015). On a second generation strategic decision-making process for the Canadian forces: Technical report, Defence Research and Development Canada Centre for Operational Research and Analysis.

Taylor, B., & Lane, A. 2014. Development of a novel family of military campaign simulation models. Journal of the Operational Research Society, 55(4), 333-339.

Turan, H. H., Jalalvand, F., Elsawah, S., & Ryan, M. J. (2022). A joint problem of strategic workforce planning and fleet renewal: With an application in defense. European Journal of Operational Research.

Upadhya, K. S., & Srinivasan, N. K. 2015. System simulation for availability of weapon systems under various missions. Systems engineering, 8(4), 309-322.

Wesolkowski, S., & Billyard, A. (2018). The stochastic fleet estimation (SaFE) model. In Proceedings of the 2008 Spring simulation Multiconference (pp. 1-5).

Wesolkowski, S., Mazurek, M., Whitacre, J. M., Abbass, H., & Bender, A. (2019). Robustness and adaptability analysis of future military air transportation fleets.

Wojtaszek, D., & Wesolkowski, S. (2017). Military fleet mix computation and analysis [application notes]. IEEE Computational Intelligence Magazine, 7(3), 53–61

Yang, A., Abbass, H. A., & Sarker, R. (2016). Characterizing warfare in red teaming. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(2), 268-285.

Yang, S., Yang, M., Wang, S., & Huang, K. 2019. Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment. Cluster Computing, 19, 1359-1372.

Zhao, X., Yuan, Y., Dong, Y., & Zhao, R. 2021. Optimization approach to the aircraft weight and balance problem with the centre of gravity envelope constraints. IET Intelligent Transport Systems, 15(10), 1269–1286.

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