Value-at-Risk model for hazardous material transportation
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This paper introduces a Value-at-Risk (VaR) model to generate route choices for a hazmat shipment based on a specified risk confidence level. VaR is a threshold value such that the probability of the loss exceeding the VaR value is less than a given probability level. The objective is to determine a route which minimizes the likelihood that the risk will be greater than a set threshold. Several properties of the VaR model are established. An exact solution procedure is proposed and tested to solve the single-trip problem. To test the applicability of the approach, routes obtained from the VaR model are compared with those obtained from other hazmat objectives, on a numerical example as well as a hazmat routing scenario derived from the Albany district of New York State. Depending on the choice of the confidence level, the VaR model gives different paths from which we conclude that the route choice is a function of the level of risk tolerance of the decision-maker. Further refinements of the VaR model are also discussed.
KeywordsHazardous materials transportation Value-at-Risk Social risk mitigation
This research was partially supported by National Science Foundation grant CMMI-1068585. The authors are grateful to Iakovos Toumazis for his help in data management and numerical computation.
- Abkowitz, M., Eiger, A., & Srinivasan, S. (1984). Estimating the release rates and costs of transporting hazardous waste. Transportation Research Record, 977, 22–30. Google Scholar
- Abkowitz, M., Lepofsky, M., & Cheng, P. (1992). Selecting criteria for designating hazardous materials highway routes. Transportation Research Record, 1333, 30–35. Google Scholar
- Alp, E. (1995). Risk-based transportation planning practice: overall methodology and a case example. INFOR. Information Systems and Operational Research, 33(1), 4–19. Google Scholar
- Droz, R. (2009). US highways: from US 1 to US 830—termini and lengths in miles. http://www.us-highways.com/us1830.htm.
- Federal Highway Administration (2007). FHWA route log and finder list—interstate system—design. http://www.fhwa.dot.gov/reports/routefinder/index.cfm.
- Jorion, P. (2007). Value at risk: the New Benchmark for managing financial risk (3rd ed.). New York: McGraw-Hill. Google Scholar
- Ju, Y., Yang, G., Liang, G., & Feng, Y. (2002). VaR and its application in civil aviation transportation. In Proceedings of the 4th world congress on intelligent control and automation (Vol. 1, pp. 677–681), Shanghai, China, June 10–14. IEEE. doi: 10.1109/WCICA.2002.1022199.
- Kauffman, R. J., & Sougstad, R. (2007). Value-at-risk in IT services contracts. doi: 10.1109/HICSS.2007.603.
- Knuth, D. E. (1998). Art of computer programming, vol. 3: Sorting and searching (2nd ed.). Reading: Addison-Wesley. Google Scholar
- Larsen, N., Mausser, H., & Uryasev, S. (2001). Algorithms for optimization of value-at-risk (Research Report 2001-9). ISE Dept., University of Florida. Google Scholar
- Linsmeier, T. J., & Pearson, N. D. (1996). Risk measurement: an introduction to value at risk. Finance, EconWPA. Google Scholar
- Manfredo, M. R., & Leuthold, R. M. (1998). Agricultural applications of value-at-risk analysis: a perspective. Finance, 9805002, EconWPA. Google Scholar
- McIlroy, P. M., Bostic, K., & McIlroy, M. D. (1993). Engineering radix sort. Computing Systems, 6(1), 5–27. Google Scholar
- McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: concepts, techniques, and tools. Princeton: Princeton University Press. Google Scholar
- Pohl, I. (1971). Bi-directional search. Machine Intelligence, 6, 127–140. Google Scholar
- Saccomanno, F., & Chan, A. (1985). Economic evaluation of routing strategies for hazardous road shipments. Transportation Research Record, 1020, 12–18. Google Scholar
- Shebl, G. B., & Berleant, D. (2002). Bounding the composite value at risk for energy service company operation with DEnv, and interval-based algorithm. In SIAM workshop on validated computing 2002 (extended abstracts) (pp. 23–25). Google Scholar
- Sivakumar, R., Batta, R., & Karwan, M. (1993a). Establishing credible risk criteria for transporting extremely dangerous hazardous materials. In F. Saccomanno & K. Cassidy (Eds.), Transportation of dangerous goods: assessing the risks, Institute for Risk Research, University of Waterloo, Canada (pp. 335–342). Google Scholar
- Sivakumar, R., Batta, R., & Karwan, M. (1995). A multiple route conditional risk model for transporting hazardous materials. INFOR. Information Systems and Operational Research, 33(1), 20–33. Google Scholar
- US Census Bureau (2010). United States Census 2010. http://2010.census.gov/2010census/.
- US Department of Transportation Pipeline, & Materials Safety Administration, H. (2012). Yearly incident summary reports. http://www.phmsa.dot.gov/hazmat/library/data-stats/incidents. Accessed August 31, 2012.