Presenting a robust optimization model for the problem of scheduling the improvement and transfer of the military transport fleet in the armed forces

Document Type : Original Article

Authors

1 Industrial Engineering, Faculty of Industrial Engineering, Islamic Azad University, Parand Branch, Tehran

2 Department of Industrial Engineering, Islamic Azad University, Parand branch

Abstract

Combined problems for the transport fleet in the armed forces organization are often used when the speed of developments of new war technologies as well as the establishment of new navigation systems; The command of the armed forces has made it necessary to modernize the operational facilities and decommission the old and ineffective military systems during a pre-planned period of time. In this research, to solve the combined problem of the war fleet, a stable optimization model is proposed to the decision makers of the defense affairs of the armed forces using simulation tools in order to improve and upgrade the military transport fleet. The objective function in the proposed model provides a way to minimize the cost of the entire military transport fleet while taking into account all the constraints, as well as the optimal movement and timely transfer of troops and weapons to operational areas. The findings of this research show that the development of the proposed approach can maximize the efficiency of the military fleet by minimizing the cost and time by presenting two possible scenarios in the Mojah area. In this research, it is also discussed to provide an option to optimize the calculation rate using meta-innovative algorithms.

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References

اصغری سراسکانرود، صیاد. موسوی، میر نجف. مهدوی، سجاد. (1398). تحلیل عوامل ژئومورفولوژیکی در مکان‌یابی مراکز نظامی- دفاعی با استفاده از ANP و GIS منطقه مورد مطالعه: پادگان‌های شهرستان‌های مرزی استان آذربایجان غربی. فصلنامه آمایش جغرافیایی فضا، دوره 9، شماره 33، صص77 -96.
الماسیان، حامد. شکیبا منش، علیرضا. (1393): چیدمان سایت‌های موشکی ساحلی و شناوری جهت تخصیص سلاح علیه اهداف سطحی دریایی، هشتمین کنفرانس ملی فرماندهی و کنترل ایران (c4i).
باشکوه آجیرلو، محمد. غفارلو، اکبر. (1401): بررسی قابلیت‌های مدیریت لجستیک نیروهای مسلح ج.ا.ایران در کاهش آسیب‌ها در بحران‌های طبیعی. فصلنامه راهبرد دفاعی. دوره 20، شماره 77، صص 73-98.
Abbass, H., Baker, S., Bender, A., & Sarker, R. (2011). Identifying the fleet mix in a military setting. In The second international intelligent logistics systems conference (pp  22–23).
Ali, I. (2021). Evolutionary algorithms for resource constrained project scheduling: Tech. rep., (p. 63). Univ. New South Wales, Canberra, NSW, Australia.
Ausseil, R., Gedik, R., Bednar, A., & Cowan, M. (2020). Identifying sufficient deception in military logistics. Expert Systems with Applications, 141, Article 112974.
Baker, J. E. (2001). Reducing bias and inefficiency in the selection algorithm. In Proceedings of the second international conference on genetic algorithms, vol. 206 (pp. 14–21).
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.
Bracco, S., Bianco, G., Siri, S., Barbagelata, C., Casati, C., & Siri, E. (2021). Simulation models for the evaluation of energy consumptions of electric buses in different urban traffic scenarios. In 2021 sixteenth international conference on ecological vehicles and renewable energies (pp. 1–6).
Gill, A. W., Egudo, R. R., Dortmans, P. J., & Grieger, D. (2007). Using agent based distillations in support of the Army capability development process-a case study: Technical report, Defence Science and Technology Organisation Salisbury (Australia) Systems Sciences Lab.
Hocaoğlu, M. F. (2021). Agent-based target evaluation and fire doctrine: An aspectoriented programming view. The Journal of Defense Modeling and Simulation, Article 15485129211040369.
Islam, M. A., Gajpal, Y., & Elmekkawy, T. Y. (2021). Mixed fleet based green clustered logistics problem under carbon emission cap. Sustainable Cities and Society, Article 103074.
Junor, L. J. (2022). Managing military readiness, no. 23. Government Printing Office.
McLucas, A., Lyell, D., & Rose, B. (2011). Defence capability management: Introduction into service of multi-role helicopters. In Proceedings of the 24th international conference of the system dynamics society (pp. 92–110).
Pereira, D. P., Gomes, I. L., Melicio, R., & Mendes, V. M. (2021). Planning of aircraft fleet maintenance teams. Aerospace, 8(5), 140.
Petitprez, E., Georges, F., Raballand, N., & Bertrand, S. (2021). Deployment optimization of a fleet of drones for routine inspection of networks of linear infrastructures. In 2021 international conference on unmanned aircraft systems (pp. 303–310).
Seshadri, A. (2011). Multi-objective optimization using evolutionary algorithms (MOEA). (p. 38).
Shah, A. I. J., Yusoff, N. M., & Noor, N. M. (2022). Optimization of Sukhoi Su-30MKM maintenance planning for maximum operational readiness. In TENCON 2017 – 2017 IEEE region 10 conference (pp. 2500–2503).
Turan, H. H., Elsawah, S., Jalalvand, F., & Ryan, M. J. (2022). Solving strategic military workforce planning problems with simulation-optimization.
Wang, Y., Limmer, S., Van Nguyen, D., Olhofer, M., Bäck, T., & Emmerich, M. (2021). Optimizing the maintenance schedule for a vehicle fleet: A simulation-based case study. Engineering Optimization, 1–14.
Zhang, H., Ge, H., Yang, J., & Tong, Y. (2021). Review of vehicle routing problems: Models, classification and solving algorithms. Archives of Computational Methods in Engineering, 1–27.
Defense Policy
Volume 33, Issue 128 - Serial Number 3
Autumn 2023
November 2024
Pages 11-38

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History

  • Receive Date: 25 March 2023
  • Revise Date: 31 May 2023
  • Accept Date: 10 December 2024

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