Applying D numbers in risk assessment process: General approach

Authors

  • Darko Božanić Military Academy, University of Defense in Belgrade, Belgrade
  • Dragan Pamučar Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
  • Nenad Komazec Military Academy, University of Defence in Belgrade, Belgrade, Serbia

DOI:

https://doi.org/10.31181/jdaic10025122023b

Keywords:

risk assessment, D numbers, decision-making

Abstract

Risk assessment is performed in different conditions and for different purposes and very often it is followed by various types of uncertainty. Sometimes uncertainty is smaller, but usually during risk assessment a large number of factors appears with incomplete information to a greater or lesser extent. Risk assessment under conditions of uncertainty is less complex with the application of various mathematical fields dealing well with uncertainty. This paper presents one approach in the application of D numbers in the process of risk assessment, respectively, risk quantification. As is known, D numbers treat uncertainty very well, so this feature of them is also used in risk quantification. In this paper, first of all are presented basic terms related to the concept of risk, as well as of D numbers. The focus of the paper is on the examples in which risk assessment is presented using D numbers. In addition to the level of risk that is defined through the application of D numbers and standard tables for risk assessment, the paper also defines the level of optimistic or pessimistic risk. Thus, in addition to the risk value, the risk interval can also be obtained, which in conditions of uncertainty provides a much more realistic picture of the problem.

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References

Avakumović, Č., Milinković, S., & Vujačić, N. (2010). Risk Management. The Proceedings of the International Scientific Conference Management (pp. 387-390). Kruševac, Serbia.

Božanić, D., & Pamučar, D. (2010). Еvaluating locations for river crossing using fuzzy logic. Military Technical Courier, 58(1), 129-145.

Božanić, D., Pamučar D., & Komazec, N. (2020). Risk assessment by applying D numbers. The Proceedings of the 6th International scientific conference safety and crisis management -Theory and practise (SecMan) (pp. 208-215). Belgrade, Serbia.

Božanić, D., Slavković, R., & Karović, S. (2015). Model of Fuzzy Logic Application to the Assessment of Risk in Overcoming the Obstacles during an Army Defensive Operation. Vojno delo, 67(4), 240-260.

Cao, J., & Song, W. (2016). Risk assessment of co-creating value with customers: A rough group analytic network process approach. Expert Systems with Applications, 55, 145-156.

Chapman, C. B, & Cooper, D. F. (1983). Risk analysis: testing some prejudices. European Journal of Operational Research, 14, 38-247.

Dabic-Miletic, S., & Simic, V. (2023). Smart and Sustainable Waste Tire Management: Decision-Making Challenges and Future Directions. Decision Making Advances, 1(1), 10–16.

Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematical Statistics, 38, 325–339.

Deng, X., Hu, Y., & Deng, Y. (2014a). Bridge condition assessment using D numbers. The Scientific World Journal, 2014, 358057.

Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014b). Environmental impact assessment based on D numbers. Expert Systems with Applications, 41, 635–643.

Erdem, F. (2022). Risk assessment with the fuzzy logic method for Ankara OIZ environmental waste water treatment plant. Turkish Journal of Engineering, 6(4), 268-27.

Gogoi, M. K., & Chutia, R. (2022). Fuzzy risk analysis based on a similarity measure of fuzzy numbers and its application in crop selection. Engineering Applications of Artificial Intelligence, 107, 104517.

Karimi, T., & Yahyazade, Y. (2022). Developing a risk assessment model for banking software development projects based on rough-grey set theory. Grey Systems: Theory and Application, 12(3), 574-594.

Karović, S., & Komazec, N. (2010). Risk management as a prerequisite of the integrated management system in organizations. Military Technical Courier, 58(3), 146-161.

Keković, Z., Komazec, N., & Jeftić, Z. (2011a). Risks of non-military security threats relevant for the third mission of the Army of Serbia. Vojno delo, 63(3), 242-257.

Keković, Z., Savić, S., Komazec, N., Milošević, M., & Jovanović, D. (2011b). Risk assessment in the protection of persons, property and business (Only in Serbian: Procena rizika u zaštiti lica, imovine i poslovanja)- Belgrade: Center for Risk Analysis and Crisis Management.

Keshavarz, E., Mahmoodirad, A., & Niroomand, S. (2023). A Transportation Problem Considering Fixed Charge and Fuzzy Shipping Costs. Decision Making Advances, 1(1), 115–122.

Khan, S., Haleem, A., & Khan, M. I. (2023). Risk assessment model for halal supply chain using an integrated approach of IFN and D number. Arab Gulf Journal of Scientific Research, 41(3), 338-358.

Kozarević, S., & Puška, A. (2015). Correlation of implementation of supply chain, partnerships and competitiveness. Ekonomska misao i praksa, 2, 579-596.

Liu, B., & Deng, Y. (2019). Risk Evaluation in Failure Mode and Effects Analysis Based on D Numbers Theory. International Journal of Computers Communications & Control, 14(5), 672-691.

Moreno-Cabezali, B. M., & Fernandez-Crehuet, J. M. (2020). Application of a fuzzy-logic based model for risk assessment in additive manufacturing R&D projects. Computers & Industrial Engineering, 145, 106529.

Nguyen, H. D., & Macchion, L. (2023). A comprehensive risk assessment model based on a fuzzy synthetic evaluation approach for green building projects: the case of Vietnam. Engineering, Construction and Architectural Management, 30(7), 2837-2861.

Pamučar, D, Božanić, D., & Đorović, B. (2011). Fuzzy logic in decision making process in the Armed Forces of Serbia. Saarbrücken: Lambert Academic Publishing.

Pamučar, D., Božanić, D., & Komazec, N. (2016a). Risk Assessment of Natural Disasterts using Fuzzy Logic System Type-2. Management - Journal for Theory and Practice Management, 21(80), 23-32

Pamučar, D., Božanić, D., & Milić, A. (2016b). Selection of a course of action by Obstacle Employment Group based on a fuzzy logic system. Yugoslav Journal of Operations Research, 26(1), 75-90

Pamučar, D., Đorović, B., Božanić, D., & Ćirović, G. (2012). Modification of the dynamic scale of marks in analytic hierarchy process (AHP) and analytic network approach (ANP) through application of fuzzy approach. Scientific Research and Essays, 7(1), 24-37.

Petrović, D.V., Tanasijević, M., Stojadinović, S., Ivaz, J., & Stojković, P. (2020). Fuzzy Model for Risk Assessment of Machinery Failures. Symmetry, 12(4), 525.

Pribićević, I., Doljanica, S., Momčilović, O., Kumar, Das, D., Pamučar, D., & Stević, Ž. (2020). Novel Extension of DEMATEL Method by Trapezoidal Fuzzy Numbers and D Numbers for Management of Decision-Making Processes. Mathematics, 8(5), 812.

Puška, A. (2011). Sensitivity analysis in the function of investment decision-making (Analiza osjetljivosti u funkciji investicijskog odlučivanja). Praktični menadžment, 2(3), 80-86.

Puška, A., Šadić, S., Maksimović, A., & Stojanović, I. (2020). Decision support model in the determination of rural touristic destination attractiveness in the Brčko District of Bosnia and Herzegovina. Tourism and Hospitality Research, 20(4), 387-405.

Radovanović, M., Petrovski, A., Behlić, A., Perišić, M., Samopjan, M., & Lakanović, B. (2023). Application model of MCDM for selection of automatic rifle. Journal of Decision Analytics and Intelligent Computing, 3(1), 185–196.

Sarwar, M., Gulzar, W. & Ashraf, S. (2023). Improved risk assessment model based on rough integrated clouds and ELECTRE-II method: an application to intelligent manufacturing process. Granular Computing, 8, 1533–1560 (2023).

Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.

Song, W., Li, J., Li, H., & Ming, X. (2020). Human factors risk assessment: An integrated method for improving safety in clinical use of medical devices. Applied Soft Computing, 86, 105918.

Tepe, S., & Kaya, I. (2020). A fuzzy-based risk assessment model for evaluations of hazards with a real-case study. Human and Ecological Risk Assessment: An International Journal, 26(2), 512-537.

Tešić, D., & Marinković, D. (2023). Application of fermatean fuzzy weight operators and MCDM model DIBR-DIBR II-NWBM-BM for efficiency-based selection of a complex combat system. Journal of Decision Analytics and Intelligent Computing, 3(1), 243–256.

Tian, J., & Yan, Z. F. (2013). Fuzzy Analytic Hierarchy Process for Risk Assessment to General-assembling of Satellite. Journal of Applied Research and Technology, 11(4), 568-577.

Zhou, J., Su, X., & Qian, H. (2020). Risk Assessment on Offshore Photovoltaic Power Generation Projects in China Using D Numbers and ANP. IEEE Access, 8, 144704-144717.

Published

25.12.2023

How to Cite

Božanić, D., Pamučar, D., & Komazec, N. (2023). Applying D numbers in risk assessment process: General approach. Journal of Decision Analytics and Intelligent Computing, 3(1), 286–295. https://doi.org/10.31181/jdaic10025122023b