Defensive Future Studies

Defensive Future Studies

Evaluating the Coronavirus effects on the Air Force of the Islamic Republic of Iran with R.Graph-TOPSIS methodology

Document Type : Original Article

Authors
Hamidreza Seiti 1
Mehran Khalaj 2
Esmaeel Sharifan 3
1 Researcher, Supreme National Defense University, Tehran, Iran
2 Ph.D. in Industrial Engineering, Islamic Azad University Robatkarim Branch, Robatkarim, Iran
3 Ph.D. in Defense Policy, Supreme National Defense University, Tehran, Iran
10.22034/dfsr.2021.528618.1486
Abstract
Defense organizations need intelligence to increase their preparedness and agility in the face of various threats and consequences, especially in cases where an event leads to a series of different consequences. One of these intelligent cases is knowing the amount of changes and variables of interest to the organization due to the occurrence of various events. In these cases, awareness of these changes provides managers with appropriate planning to reduce them. There are various methods for scenario analysis and causal chain analysis in the literature that have their strengths and weaknesses. In this study, a scenario analysis framework based on the  R.Graph  and TOPSIS methods was presented to investigate the effects of Coronavirus on the Air Force  Army of the Islamic Republic of Iran (NAHAJA). The results show that due to the Coronavirus the most important among the variables is related to the factor of disruption in skill-based training and the least importance belongs to the total cost variable. Based on these results, decision-makers can establish appropriate policies to reduce these consequences.
Keywords
R.Graph method
TOPSIS method
Risk management
COVID-19
NAHAJA

  •  

    • Chen, K., Ren, Z., Mu, S., Sun, T. Q., & Mu, R. (2020). Integrating the Delphi survey into scenario planning for China's renewable energy development strategy towards 2030. Technological Forecasting and Social Change, 158: 120157.
    • Derbyshire, J., & Giovannetti, E. (2017). Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting. Technological Forecasting and Social Change, 125: 334-344.
    • Hafezalkotob, A., & Hafezalkotob, A. (2016). Fuzzy entropy-weighted MULTIMOORA method for materials selection. Journal of Intelligent & Fuzzy Systems, 31(3): 1211-1226.
    • Hussain, M., Tapinos, E., & Knight, L. (2017). Scenario-driven roadmapping for technology foresight. Technological Forecasting and Social Change, 124: 160-177.
    • Johansen, I. (2018). Scenario modelling with morphological analysis. Technological Forecasting and Social Change, 126: 116-125.
    • Kaedi, M., Ghasem-Aghaee, N., & Ahn, C. W. (2016). Biasing the transition of Bayesian optimization algorithm between Markov chain states in dynamic environments. Information Sciences, 334: 44-64.
    • MacKay, R. B., & Stoyanova, V. (2017). Scenario planning with a sociological eye: Augmenting the intuitive logics approach to understanding the Future of Scotland and the UK. Technological Forecasting and Social Change, 124: 88-100.
    • Norouzi, N., Fani, M., & Ziarani, Z. K. (2020). The fall of oil Age: A scenario planning approach over the last peak oil of human history by 2040. Journal of Petroleum Science and Engineering, 188: 106827.
    • Oliva, S. V., & Martinez-Sanchez, A. (2018). Technology roadmapping in security and defence foresight. foresight.
    • Potîrniche, M. T. (2017). Military Scenario Development. Vojenské rozhledy, 26(MC): 33-40.
    • Saritas, O., & Burmaoglu, S. (2016). Future of sustainable military operations under emerging energy and security considerations. Technological Forecasting and Social Change, 102: 331-343.
    • Seiti, H., Makui, A., Hafezalkotob, A., Khalaj, M. & A. Hameed, I. (2021). R. Graph: A New Risk-based Causal Reasoning and Its Application to COVID-19 Risk Analysis. VIXRA preprint VIXRA: 0020. https://vixra.org/abs/2102.0020
    • Tack, S. (2020). COVID-19: How Pandemics Disrupt Military Operations (2020). Extracted from https://www.realcleardefense.com/articles/2020/03/28/covid-19_how_pandemics_disrupt_military_operations_115157.html.
    • Wang, H., Xu, C., & Xu, Z. (2019). An approach to evaluate the methods of determining experts’ objective weights based on evolutionary game theory. Knowledge-Based Systems, 182: 104862.
    • Washingtonpost. (2020). How coronavirus will affect the US military Extracted from,https://www.washingtonpost.com/politics/2020/03/19/how-will-coronavirus-affect-us-military-readiness-national-security/.
    • Witt, T., Dumeier, M., & Geldermann, J. (2020). Combining scenario planning, energy system analysis, and multi-criteria analysis to develop and evaluate energy scenarios. Journal of Cleaner Production, 242: 118414.

    Yue, Z. (2011). A method for group decision-making based on determining weights of decision makers using TOPSIS. Applied Mathematical Modelling, 35(4), 1926-1936.