Bamakan, S. M. H., & Dehghanimohammadabadi, M. (2015). A weighted Monte Carlo simulation approach to risk assessment of information security management system. International Journal of Enterprise Information Systems (IJEIS), 11(4), 63-78.
Cummins, E., Butler, F., Gormley, R., & Brunton, N. (2009). A Monte Carlo risk assessment model for acrylamide formation in French fries. Risk Analysis: An International Journal, 29(10), 1410-1426.
Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International journal of production economics, 182, 144-156.
Gachlou, M., Roozbahani, A., & Banihabib, M. E. (2019). Comprehensive risk assessment of river basins using Fault Tree Analysis. Journal of hydrology, 577, 123974.
Huang, Z. D., Chang, W. B., Xiao, Y. Y., & Liu, R. (2010). An extended Monte Carlo method on simulating the development cost uncertainties of aircraft. Advanced Materials Research, 118, 810-814.
Jamshidi, A., Ait-Kadi, D., Ruiz, A., & Rebaiaia, M. L. (2018). Dynamic risk assessment of complex systems using FCM. International Journal of Production Research, 56(3), 1070-1088.
Kim, Y. J. (2017). Monte Carlo vs. fuzzy Monte Carlo simulation for uncertainty and global sensitivity analysis. Sustainability, 9(4), 539.
Liu, H., Deng, X., & Jiang, W. (2017). Risk evaluation in failure mode and effects analysis using fuzzy measure and fuzzy integral. Symmetry, 9(8), 162.
Nakandala, D., Lau, H., & Zhao, L. (2017). Development of a hybrid fresh food supply chain risk assessment model. International Journal of Production Research, 55(14), 4180-4195.
Pouillot, R., Beaudeau, P., Denis, J. B., Derouin, F., & AFSSA Cryptosporidium Study Group. (2004). A quantitative risk assessment of waterborne cryptosporidiosis in France using second‐order Monte Carlo simulation. Risk Analysis: An International Journal, 24(1), 1-17.
Rezaie, K., Amalnik, M. S., Gereie, A., Ostadi, B., & Shakhseniaee, M. (2007). Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties. Applied Mathematics and Computation, 190(2), 1492-1501.
Rezaie, K., Gereie, A., Ostadi, B., & Shakhseniaee, M. (2009). Safety interval analysis: A risk-based approach to specify low-risk quantities of uncertainties for contractor’s bid proposals. Computers & Industrial Engineering, 56(1), 152-156.
Sarbayev, M., Yang, M., & Wang, H. (2019). Risk assessment of process systems by mapping fault tree into artificial neural network. Journal of Loss Prevention in the Process Industries, 60, 203-212.
Schuhmacher, M., Meneses, M., Xifró, A., & Domingo, J. L. (2001). The use of Monte-Carlo simulation techniques for risk assessment: study of a municipal waste incinerator. Chemosphere, 43(4-7), 787-799.
Shorter, J. A., & Rabitz, H. A. (1997). Risk analysis by the guided Monte Carlo technique. Journal of Statistical Computation and Simulation, 57(1-4), 321-336.
Wang, Y. M., & Elhag, T. M. (2007). A fuzzy group decision making approach for bridge risk assessment. Computers & Industrial Engineering, 53(1), 137-148.