Data Envelopment Analysis (DEA) for Modeling Efficiency in the Deployment of Military Units for Humanitarian Missions

Document Type : Original Article

Authors

1 Corresponding Author, Assistance Prof, Department of Science and Technology, University of Command and Staff, Tehran, Iran. Email: n.shamami@casu.ac.ir

2 Assistance Prof, Department of Science and Technology, University of Command and Staff, Tehran, Iran. Email: masoud.vaseei@iau.ac.ir

3 Assistance Prof, Department of Industrial Engineering, Imam Ali Officer University, Tehran, Iran. Email: omid_vte@gmail.com

10.22091/jemsc.2026.15323.1349

Abstract

Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of decision-making units with similar functions operating under comparable conditions. In humanitarian missions, particularly during crises, identifying efficient patterns for deploying military units is critical to the speed and effectiveness of rescue operations. However, uncertainty in environmental conditions and field information can reduce the accuracy of efficiency measurement. This research proposes a DEA-based framework to evaluate and optimize the deployment of military units in humanitarian operations using bootstrap simulation. A three-stage DEA approach combined with a bootstrap method, grounded in natural, managerial, and free accessibility principles, is applied to data collected from active operational units in a real-world crisis response. Results indicate that under deterministic data only a subset of units is efficient, while many are classified as locationally inefficient. After generating simulated data and removing environmental noise, efficiencies are recalculated and comparative changes in unit performance are observed. These findings support more reliable decision-making and provide practical guidance for planners seeking robust, data-driven deployment strategies under uncertainty in complex humanitarian crisis environments.

Keywords

Main Subjects

References

Abolghasemian, M., Kanai, A. G., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies, 13(4), 705-732. https://doi.org/10.22059/ijms.2020.294809.673898
Abolghasemian, M., Pourghader Chobar, A., AliBakhshi, M., Fakhr, A., & Moradi Pirbalouti, S. (2021). Delay scheduling based on discrete-event simulation for construction projects. Iranian Journal of Operations Research, 12(1), 49-63.
Anaya-Arenas, A. M., Ruiz, A., & Renaud, J. (2018). Importance of fairness in humanitarian relief distribution. Production Planning & Control, 29(14), 1145-1157.
Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498.
Cao, C., Liu, Y., Tang, O., & Gao, X. (2021). A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics, 235, 108081.
Eligüzel, İ. M., Özceylan, E., & Weber, G. W. (2023). Location-allocation analysis of humanitarian distribution plans: A case of United Nations Humanitarian Response Depots. Annals of Operations Research, 324(1-2), 825-854.
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: Analysis of the emergency department resources under COVID-19 conditions. International Journal of Industrial and Systems Engineering, 43(1), 1-19.
Jahangiri, S., Abolghasemian, M., Pourghader Chobar, A., Nadaffard, A., Mottaghi, V. (2021). Ranking of key resources in the humanitarian supply chain in the emergency department of iranian hospital: A real case study in COVID-19 conditions. Journal of Applied Research on Industrial Engineering, 8(Special Issue), 1-10.
Kanoun, I., Chabchoub, H., & Aouni, B. (2010). Goal programming model for fire and emergency service facilities site selection. INFOR: Information Systems and Operational Research, 48(3), 143-153.
Maghfiroh, M. F., & Hanaoka, S. (2020). Multi-modal relief distribution model for disaster response operations. Progress in Disaster Science, 6, 100095.
Maharjan, R., & Hanaoka, S. (2020). A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty. Socio-Economic Planning Sciences, 70, 100727.
Mamashli, Z., Bozorgi-Amiri, A., Dadashpour, I., Nayeri, S., & Heydari, J. (2021). A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Computing and Applications, 1-27.
Mansoori, S., Bozorgi-Amiri, A., & Pishvaee, M. S. (2020). A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications, 32(7), 2183-2203.
Motamedi, M., and Movahedi, M., and Rezaian, J., and Rashidi Komijani, A. (2019). Designing a non-linear mixed integer two-objective math model to maximize the reliability of blood supply chain. Engineering and Quality Management, 8(4), 259-274. [In Persian]
Narimani R, Motamedi M, Amoozad khalili H. (2023). Applying a mathematical model for the distribution of earthquake relief items to the affected areas of Tehran. Disaster Prevention and Management Knowledge. 13(2), 184-203.
Ozbay, E., Çavuş, Ö, & Kara, B. Y. (2019). Shelter site location under multi-hazard scenarios. Computers & Operations Research, 106, 102-118.
Peng, D., Ye, C., & Wan, M. (2022). A multi-objective improved novel discrete particle swarm optimization for emergency resource center location problem. Engineering Applications of Artificial Intelligence, 111, 104725.
Poornaser, M., Amoozadkhalili, H., & Motamedi, M. (2022). Routing disaster relief vehicles in a humanitarian supply chain. Disaster Prevention and Management Knowledge (quarterly), 12(2), 205-216.
Praneetpholkrang, P., & Huynh, V. N. (2020, February). Shelter site selection and allocation model for efficient response to humanitarian relief logistics. In International Conference on Dynamics in Logistics (pp. 309-318). Springer.
Rezaei Kallaj, M., Abolghasemian, M., Moradi Pirbalouti, S., Sabk Ara, M., & Pourghader Chobar, A. (2021). Vehicle routing problem in relief supply under a crisis condition considering blood types. Mathematical Problems in Engineering, 2021.
Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The pre-positioned warehouse location selection for international humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 34(4), 297-307.
Sabouhi, F., Bozorgi-Amiri, A., & Vaez, P. (2020). Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50(9), 2632-2650. https://doi.org/10.1108/K-10-2020-0632
Wang, B. C., Qian, Q. Y., Gao, J. J., Tan, Z. Y., & Zhou, Y. (2021). The optimization of warehouse location and resources distribution for emergency rescue under uncertainty. Advanced Engineering Informatics, 48, 101278.
Yofrido, F. M., & Harjana, L. T. (2019). Social-fairness perception in natural disaster, learn from Lombok: a phenomenological report. Indonesian Journal of Anesthesiology and Reanimation, 1(1), 1-7.
Zheng, Y. J., Chen, S. Y., & Ling, H. F. (2015). Evolutionary optimization for disaster relief operations: A survey. Applied Soft Computing, 27, 553-566.
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394. https://doi.org/10.1016/j.eswa.2023.122394 
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics