Volume 8, Issue 2 (Summer 2021)                   johe 2021, 8(2): 24-31 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mirmohammadsadeghi A, Abniki H, Hasanpour H. Risk Management Modeling for HazMat Transportation. johe 2021; 8 (2) :24-31
URL: http://johe.umsha.ac.ir/article-1-675-en.html
1- Department of Industrial Engineering, Faculty of Engineering, University of Imam Hussein , ammsadeghi@ihu.ac.ir
2- Department of Industrial Engineering, Faculty of Engineering, University of Imam Hussein
Abstract:   (2044 Views)
Background and Objective: Industrial development accelerates the transportation of hazardous materials, especially in developing countries, which leads to an increased risk of accidents. Consequently, conducting further research is essential to reduce these consequences. The present study aimed to provide a simple, comprehensive and innovative model of risk management for transporting hazardous materials.
Materials and Methods: The proposed model consisted of four steps of identification, analysis, evaluation, and control. Appropriate layouts and tools such as system dynamics and fuzzy AHP were used to ensure that all relevant elements were carefully examined. The model was implemented in a real example of hydrazine explosive transport.
Results: In the risk analysis stage, it was found that population density is the most important factor in increasing the risk of transporting hazardous materials along with sub-criteria related to driver and management. Finally, ways to reduce risk to an acceptable level were examined during the presentation of risk control planning.
Conclusion: Using system dynamics in the risk identification stage and fuzzy AHP in the risk analysis stage along with expert judgment can be effective in improving the risk management process.

Full-Text [PDF 1085 kb]   (1295 Downloads)    
Type of Study: Research Article | Subject: Safety

References
1. Paltrinieri N, Landucci G, Molag M, Bonvicini S, Spadoni G, Cozzani V. Risk reduction in road and rail LPG transportation by passive fire protection. J Hazard Mater. 2009;167(1-3):332-44. PMID: 19188020 DOI: 10.1016/j.jhazmat.2008.12.122 [DOI] [PubMed]
2. Bianco L, Caramia M, Giordani S. A bilevel flow model for hazmat transportation network design. Transport Res Part C Emerg Technol. 2009;17(2):175-96. DOI: 10.1016/j.trc.2008.10.001 [DOI]
3. Mearns K, Yule S. The role of national culture in determining safety performance: challenges for the global oil and gas industry. Saf Sci. 2009;47(6):777-85. DOI: 10.1016/j.ssci.2008.01.009 [DOI]
4. Oggero A, Darbra RM, Muñoz M, Planas E, Casal J. A survey of accidents occurring during the transport of hazardous substances by road and rail. J Hazard Mater. 2006;133(1-3):1-7. PMID: 16298045 DOI: 10.1016/j.jhazmat.2005.05.053 [DOI] [PubMed]
5. Aven T. Safety is the antonym of risk for some perspectives of risk. Saf Sci. 2009;47(7):925-30. DOI: 10.1016/j.ssci.2008.10.001 [DOI]
6. Zio E. An introduction to the basics of reliability and risk analysis. Hackensack, NJ: World Scientific; 2007.
7. Arabian-Hoseynabadi H, Oraee H, Tavner PJ. Failure modes and effects analysis (FMEA) for wind turbines. Int J Electrical Power Energy Syst. 2010;32(7):817-24. DOI: 10.1016/j.ijepes.2010.01.019 [DOI]
8. Feili HR, Akar N, Lotfizadeh H, Bairampour M, Nasiri S. Risk analysis of geothermal power plants using Failure Modes and Effects Analysis (FMEA) technique. Energy Conversion Manag. 2013;72:69-76. DOI: 10.1016/j. enconman.2012.10.027 [DOI]
9. Trafialek J, Kolanowski W. Application of failure mode and effect analysis (FMEA) for audit of HACCP system. Food Con. 2014;44:35-44. DOI: 10.1016/j.foodcont.2014.03.036 [DOI]
10. Omidvari M, Mansouri N, Nouri J. A pattern of fire risk assessment and emergency management in educational center laboratories. Saf Sci. 2015;73:34-42. DOI: 10.1016/j.ssci.2014.11.003 [DOI]
11. Yousefi S, Alizadeh A, Hayati J, Baghery M. HSE risk prioritization using robust DEA-FMEA approach with undesirable outputs: a study of automotive parts industry in Iran. Saf Sci. 2018;102:144-58. DOI: 10.1016/j.ssci. 2017.10.015 [DOI]
12. Kleindorfer PR, Saad GH. Managing disruption risks in supply chains. Product Operat Manage. 2005;14(1):53-68. DOI: 10.1111/j.1937-5956.2005.tb00009.x [DOI]
13. Adhitya A, Srinivasan R. Dynamic simulation and decision support for multisite specialty chemicals supply chain. Ind Eng Chem Res. 2010;49(20):9917-31. DOI: 10.1021/ie100170j [DOI]
14. Laínez JM, Puigjaner L. Prospective and perspective review in integrated supply chain modelling for the chemical process industry. Curr Opin Chem Eng. 2012;1(4):430-45. DOI: 10.1016/j.coche.2012.09.002 [DOI]
15. Li YZ, Hu H, Huang DZ. Developing an effective fuzzy logic model for managing risks in marine oil transport. Int J Ship Transport Logistics. 2013;5(4-5):485-99. DOI: 10.1504/IJSTL.2013.055286 [DOI]
16. Peng M, Peng Y, Chen H. Post-seismic supply chain risk management: a system dynamics disruption analysis approach for inventory and logistics planning. Computs Oper Res. 2014;42(2014):14-24. DOI: 10.1016/j.cor.2013.03.003 [DOI]
17. Radivojević G, Gajović V. Supply chain risk modeling by AHP and Fuzzy AHP methods. J Risk Res. 2014;17(3):337-52. DOI: 10.1080/13669877.2013.808689 [DOI]
18. Liu LP, Li SX, Fan TJ, Chang XY. Transportation risk assessment of chemical industry supply chain based on a dual model. Proc Environ Sci. 2011;11:393-7. DOI: 10.1016/j.proenv.2011.12.063 [DOI]
19. Kazantzi V, Kazantzis N, Gerogiannis VC. Risk informed optimization of a hazardous material multi-periodic transportation model. J Loss Prev Proc Ind. 2011;24(6):767-73. DOI: 10.1016/j.jlp.2011.05.006 [DOI]
20. Pradhananga R, Taniguchi E, Yamada T, Qureshi AG. Bi-objective decision support system for routing and scheduling of hazardous materials. Soc Econ Plan Sci. 2014;48(2):135-48. DOI: 10.1016/j.seps.2014.02.003 [DOI]
21. Bronfman A, Marianov V, Paredes-Belmar G, Lüer-Villagra A. The maximin HAZMAT routing problem. Eur J Operat Res. 2015;241(1):15-27. DOI: 10.1016/j.ejor.2014.08.005 [DOI]
22. Forigua J, Lyons L. Safety analysis of transportation chain for dangerous goods: a case study in Colombia. Transport Res Proc. 2016;12:842-50. DOI: 10.1016/j.trpro.2016.02.037 [DOI]
23. Yang Q, Chin KS, Li YL. A quality function deployment-based framework for the risk management of hazardous material transportation process. J Loss Prev Proc Ind. 2018;52:81-92. DOI: 10.1016/j.jlp.2018.02.001 [DOI]
24. Gul M, Guneri AF, Nasirli SM. A fuzzy-based model for risk assessment of routes in oil transportation. Int J Environ Sci Technol. 2019;16(8):4671-86. DOI: 10.1007/s13762-018-2078-z [DOI]
25. Ghaleh S, Omidvari M, Nassiri P, Momeni M, Lavasani SM. Pattern of safety risk assessment in road fleet transportation of hazardous materials (oil materials). Saf Sci. 2019;116:1-2. DOI: 10.1016/j.ssci.2019.02.039 [DOI]
26. Jabbari M, Atabi F, Ghorbani R. Key airborne concentrations of chemicals for emergency response planning in HAZMAT road transportation-margin of safety or survival. J Loss Prev Proc Ind. 2020;65:104139. DOI: 10.1016/j.jlp.2020.104139 [DOI]
27. Ferrio J, Wassick J. Chemical supply chain network optimization. Comput Chem Eng. 2008;32(11):2481-504. DOI: 10.1016/j.compchemeng.2007.09.002 [DOI]
28. You F, Wassick JM, Grossmann IE. Risk management for a global supply chain planning under uncertainty: models and algorithms. AIChE J. 2009;55(4):931-46. DOI: 10.1002/aic.11721 [DOI]
29. Tong K, Feng Y, Rong G. Planning under demand and yield uncertainties in an oil supply chain. Ind Eng Chem Res. 2012;51(2):814-34. DOI: 10.1021/ie200194w [DOI]
30. Laínez JM, Puigjaner L, Reklaitis GV. Financial and financial engineering considerations in supply chain and product development pipeline management. Comput Chem Eng. 2009;33(12):1999-2011. DOI: 10.1016/j.compche meng.2009.06.025 [DOI]
31. Carneiro MC, Ribas GP, Hamacher S. Risk management in the oil supply chain: a CVaR approach. Ind Eng Chem Res. 2010;49(7):3286-94. DOI: 10.1021/ie901265n [DOI]
32. Oliveira F, Hamacher S. Optimization of the petroleum product supply chain under uncertainty: a case study in northern Brazil. Ind Eng Chem Res. 2012;51(11):4279-87. DOI: 10.1021/ie2013339 [DOI]
33. European agreement concerning the international carriage of dangerous goods by road. The Agreement concerning the International Carriage of Dangerous Goods by Road (ADR). Available at: URL: https://www.unece.org/fileadmin/ DAM/trans/danger/publi/adr/adr2015/ADR2015e_WEB.pdf; 2005. [Article]
34. Omidvari M, Nourmoradi H, Nouri J, Shamaii A. Presenting the pattern of occupational and environmental health risk assessment in oil products transportation. Health Syst Res. 2013;9(2):177-87.
35. Benekos I, Diamantidis D. On risk assessment and risk acceptance of dangerous goods transportation through road tunnels in Greece. Saf Sci. 2017;91:1-10. DOI: 10.1016/j.ssci.2016.07.013 [DOI]
36. Jabbari M, Khodaparast E, Sadri K, Kavousi A. A survey on hazardous materials accidents during road transport in Iran. Iran Occup Health. 2014;11(5):30-42.
37. Habibi A, Sarafrazi A, Izadyar S. Delphi technique theoretical framework in qualitative research. Int J Eng Sci. 2014;3(4):8-13.
38. Benekos I, Diamantidis D. On risk assessment and risk acceptance of dangerous goods transportation through road tunnels in Greece. Saf Sci. 2017;91:1-10. DOI: 10.1016/j.ssci.2016.07.013 [DOI]
39. Chang DY. Applications of the extent analysis method on fuzzy AHP. Eur J Operat Res. 1996;95(3):649-55. DOI: 10.1016/0377-2217(95)00300-2 [DOI]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | Journal of Occupational Hygiene Engineering

Designed & Developed by : Yektaweb