An Information Based Routing Model for Hazardous Material Route Selection Problem

  • Sumeet S. Desai Laureate Education Inc, Baltimore, MD
  • Gino J. Lim Department of Industrial Engineering, University of Houston


In this paper, we address some key research questions concerning the alternative routing policy of hazardous materials in real time using stochastic dynamic networks based on real life situations. The scenario that we address in this paper involves the use of sophisticated communication tools to provide information on the current condition of the optimal path and incorporate them in our optimization model to generate alternative routes for hazmat vehicles. We address the issues of designing a framework and requirements for an adaptive routing system. To overcome system instability and information overloading, a feeback based routing policy within the framework has been developed. We show the implementation of the framework and disucss the potential benefits of our approach with the help of numerical experiments based on a real hazmat transportation network.


Abkowitz, M., & Cheng, P. (1988). Developing Risk-Cost Framework for Routing of Truck Movements of Hazardous Materials. Accident Analysis and Prevention, 20(1), 29-51.

Akgun, V., Parekh, A., Batta, R., & Rump, C. (2007). Routing of a Hazmat Truck in Presence of Weather Systems. Computers and Operations Research, 34, 1351-1373.

Ashtakala, B., & Eno, L. (1996). Minimum Risk Route Models for Hazardous Materials. Journal of Transportation Engineering, 122(3), 350-357.

Bander, J., & White, C. (2002). A Heuristic Approach for Solving Non-stationary Stochastic Shortest Path Problem with Terminal Cost. Transportation Science, 36(2), 218-230.

Batta, R., & Chiu, S. (1988). Optimal Obnoxious Paths on a Network: Transportation of Hazardous Materials. Operations Research, 36(1), 84-92.

Beroggi, G., & Wallace, W. (1995). Operational Control of the Transportation of Hazardous Materials: An Assessment of Alternative Decision Models. Management Science, 41(12), 1962-1977.

Desai, S., & Lim, G. (2010). A Comparison of Multivariate Statistical Methods for Estimating the Expected Consequences for Low Probability High Consequence Events. Human Factors and Ergonomics in Manufacturing & Service Industries, 20(3), 233-250.

Desai, S., & Lim, G. (2012). Solution Rime Reduction Techniques of a Stochastic Dynamic Programming Approach for Hazardous Materials Route Selection Problem. University of Houston. Retrieved February 5, 2013, from E2Map for Natural Disasters: University of Houston:

Erkut, E., & Verter, V. (1998). Modeling of Transport Risk for Hazardous Materials. Operations Research, 46(5), 625-642.

Glickman, T. (1991). An Expeditious Risk Assessment of the Highway Transportation of Flammable Liquids. Transportation Science, 25(2), 115-123.

Glickman, T., & Rosenfield, D. (1984). Risk of Catastrophic Derailments Involving Hazardous Materials. Management Science, 30(4), 503-511.

Gopalan, R., Kolluri, K., Batta, R., & Karwan, M. (1990). Modeling Equity of Risk in the Transportation of Hazardous Materials. Operations Research, 38(6), 961-973.

Jin, H., & Batta, R. (1997). Objectives Derived from Viewing Hazmat Shipments as a Sequence of Independent Bernoulli Trials. Transporation Science, 31(3), 252-261.

Jin, H., Batta, R., & Karwan, M. (1996). On the Analysis of Two New Models for Transporting Hazardous Materials. Operations Research, 44(5), 710-723.

Kara, B., Erkut, E., & Verter, V. (2003). Accurate Calculation of Hazardous Materials Transport Risks. Operations Research Letters, 31, 285-292.

Kim, S., Lewis, M., & White, C. (2005). State Space Reduction for Non-stationary Stochastic Shortest Path Problems with Real-time Traffic Information. IEEE Transactions of Intelligent Transportation Systems, 6(3), 273-284.

Lim, G., & Desai, S. (2010). Markov Decision Process Approach for Multiple Objective Hazardous Materials Transportation Route Selection Problem. International Journal of Operational Research, 7(4), 506-529.

List, G., & Mirchandani, P. (1991). An Integrated Network/Planar Multiobjective Model for Routing and Facility Location of Hazardous Materials and Wastes. Transportation Science, 25(2), 146-156.

List, G., Mirchandani, P., Turnquist, M., & Zografos, K. (1991). Modeling and Analysis for Hazardous Material Transportation: Risk Analysis, Routing/Scheduling, and Facility Location. Transportation Science, 25(2), 100-114.

Nembhard, D., & White, C. (1997). Application of Non-order Preserving Path Selection to Hazmat Routing. Transportation Science, 31(3), 262-271.

Nozick, L., List, G., & Turnquist, M. (1997). Integrated Routing and Scheduling in Hazardous Materials Transportation. Transportation Science, 31(3), 200-215.

Patel, M., & Horowitz, A. (1994). Optimal Routing of Hazardous Material Considering Risk of Spill. Transportation Research Part A, 28A(2), 119-132.

Puterman, M. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY: John Wiley Sons.

Sherali, H., Brizendine, L., Glickman, T., & Subramanian, S. (1997). Low Probability High Consequence Considerations for Routing Hazardous Material Shipments. Transportation Science, 31(3), 237-251.

Sivakumar, R., Batta, R., & Karwan, M. (1995). A Multiple Route Conditional Risk Model for Transporting Hazardous Materials. INFOR, 33(1), 20-33.

Verter, V., & Erkut, E. (1997). Incorporating Insurance Costs in Hazardous Materials Routing Models. Transportation Science, 31(3), 227-236.

Weigkritch, K., & Fedra, E. (1995). Decision Support Systems for Dangerous Goods Transportation. INFOR, 33(2), 84-99.

Zografos, K., & Androustopoulos, K. (2004). A Heuristic Algorithm for Solving Hazardous Materials Distribution Problems. European Journal of Operational Research, 152, 507-519.

How to Cite
Desai, S. S., & Lim, G. J. (2013). An Information Based Routing Model for Hazardous Material Route Selection Problem. Industrial and Systems Engineering Review, 1(1), 1-12. Retrieved from