A novel risk assessment approach using Monte Carlo simulation based on co-occurrence of risk factors: A case study of a petrochemical plant construction

Document Type : Article

Authors

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Nowadays, because of the advancement of technology and subsequently unpredictable events, it is important for addressing risk management as an important part of projects and business. In this paper, a novel approach based on Monte Carlo simulation has been proposed for risk assessment, which considers the co-occurrence of risks. In this method, the output of extended and classic Monte Carlo simulation is applied for co-occurrence-based risk assessment (CORA) and prioritization. Also, the magnitude in each source of uncertainty has been determined by a new approach. The proposed model investigates risk’s relationship and determines the type of effect as resonance or reduction in addition to identifying and analyzing the risks. Also, a system dynamic model is applied to illustrate the relationships of risks. Finally, this method is applied to a petrochemical project. Five risks as temperature, rain, labor, cost, and inflation are considered in this project. Based on the numerical results, the most important risk is inflation. Also, there is a significant different between the result of the proposed model in comparison with model that ignore the co-occurrence of risks. CORA helps the manager to consider all aspect of risks and help them to have a better decision.

Keywords


References:
1.    Xing, X.-y., Y.-y. Xiao, and Z.-d. Huang. “Uncertainty research on economic reliability of equipment based on Extended Monte Carlo Simulation”, Industrial Engineering and Engineering Management (IE&EM), 2010 IEEE 17Th International Conference on. IEEE (2010).
2.    Cagliano, A.C., S. Grimaldi, and C. Rafele, “Choosing project risk management techniques: A theoretical framework”, Journal of Risk Research, 18(2): p. 232-248 (2015).
3.    Verbano, C. and K. Venturini, “Managing risks in SMEs: A literature review and research agenda”, Journal of technology management & innovation, 8(3): p. 186-197 (2013).
4.    Rezaie, K., et al. “Using extended Monte Carlo simulation method for the improvement of risk management: Consideration of relationships between uncertainties”, Applied Mathematics and Computation, 190(2): p. 1492-1501 (2007).
5.    Fouladgar, M.M., A. Yadani-Chamzini, and M. Basiri. “Risk evaluation of tunneling projects by fuzzy Topsis”, International conference on management (2011).
6.    Kim, Y.-J., “Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis”, Sustainability, 9(4): p. 539 (2017).
7.    Majd, M., A. Fatemi, and H. Soltanpanah, “The risk analysis of oil projects using fuzzy TOPSIS technique (Case study: 18-inch pipeline repair project from Cheshme Khosh to Ahwaz)”, Int. J. Basic Sci. Appl. Res, 3(5): p. 28 (2014).
8.    Lyu, H.-M., et al., “Risk assessment using a new consulting process in fuzzy AHP”, Journal of Construction Engineering and Management, 146(3): p. 04019112 (2020).
9.    Sohrabinejad, A. and M. Rahimi, “Risk determination, prioritization, and classifying in construction project case study: gharb tehran commercial-administrative complex”, Journal of Construction Engineering (2015).
10.    Silvestri, A., F. De Felice, and A. Petrillo, “Multi-criteria risk analysis to improve safety in manufacturing systems”, International Journal of Production Research, 50(17): p. 4806-4821 (2012).
11.    Huang, Z.D., Chang, W.B., Xiao, Y.Y. and Liu, R. “An extended Monte Carlo method on simulating the development cost uncertainties of aircraft”, Advanced Materials Research. Trans Tech Publ (2010).
12.    Chang, B., et al., “Using fuzzy analytic network process to assess the risks in enterprise resource planning system implementation”, Applied Soft Computing, 28: p. 196-207 (2015).
13.    Dehdasht, G., et al., “DEMATEL-ANP Risk Assessment in Oil and Gas Construction Projects”, Sustainability, 9(8): p. 1420 (2017).
14.    Bamakan, S.M.H. and M. Dehghanimohammadabadi, “A weighted Monte Carlo simulation approach to risk assessment of information security management system”, International Journal of Enterprise Information Systems (IJEIS), 11(4): p. 63-78 (2015).
15.    Wang, X., et al., “A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain”, International Journal of Production Economics, 135(2): p. 595-606 (2012).
16.    Zhao, L., et al., “Quantifying the fate and risk assessment of different antibiotics during wastewater treatment using a Monte Carlo simulation”, Journal of Cleaner Production, 168: p. 626-631 (2017).
17.    Liu, J., et al., “Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method”, Journal of Cleaner Production, 149: p. 945-967 (2017).
18.    Wang, Y.-M. and T.M. Elhag, “A fuzzy group decision making approach for bridge risk assessment”, Computers & Industrial Engineering, 53(1): p. 137-148 (2007).
19.    Naderpour, H., A. Kheyroddin, and S. Mortazavi, “Risk Assessment in Bridge Construction Projects in Iran Using Monte Carlo Simulation Technique”, Practice Periodical on Structural Design and Construction, 24(4): p. 04019026 (2019).
20.    Boateng, P., Z. Chen, and S.O. Ogunlana, “An Analytical Network Process model for risks prioritisation in megaprojects”, International Journal of Project Management, 33(8): p. 1795-1811 (2015).
21.    Sharma, S. and R. Pratap, “A case study of risks prioritization using FMEA method”, International Journal of Scientific and Research Publications, 3(10): p. 1-4 (2013).
22.    Rezaee, M.J., et al., “Risk analysis of sequential processes in food industry integrating Multi-stage fuzzy cognitive map and process failure mode and effects analysis”, Computers & Industrial Engineering, 123, pp. 325-337 (2018).
23.    Aqlan, F. and S.S. Lam, “Supply chain risk modelling and mitigation”, International Journal of Production Research, 53(18): p. 5640-5656 (2015).
24.    Mhatre, T.N., J. Thakkar, and J. Maiti, “Modelling critical risk factors for Indian construction project using interpretive ranking process (IRP) and system dynamics (SD)”, International Journal of Quality & Reliability Management, 34(9): p. 1451-1473 (2017).
25.    Liu, B. and F.-h. Sun, “Research on the risk assessment method of PPP project based on the improved matter element model”, Scientia Iranica, 27(2): p. 614-624 (2020).
26.    Wang, X.-x. and J.-w. Huang. “Risk analysis of construction schedule based on Monte Carlo simulation”, Computer Network and Multimedia Technology, CNMT 2009. International Symposium on. IEEE (2009).
27.    Rezaie, K., Gereie, A., Ostadi, B. and Shakhseniaeea, M. “Safety interval analysis: A risk-based approach to specify low-risk quantities of uncertainties for contractor’s bid proposals”, Computers & Industrial Engineering, 56(1): p. 152-156 (2009).