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This paper is concerned with the development and analysis of a mathematical model for determining a route that attempts to reduce the risk of low probability—high consequence accidents related with the transportation of hazardous materials. The approach adopted considers trade-offs between the conditional expectation of a catastrophic outcome given that an accident has occurred, and more traditional measures of risk dealing with the expected value of the consequence and the accident probability on a selected path. More specifically, the problem we address involves finding a path that minimizes the conditional expectation objective value, subject to the expected value of the consequence being lesser than or equal to a specified value v, and the probability of an accident on the path being also constrained to be no more than some value η. The values v and η are user-prescribed and could be prompted by the solution to the shortest path problems that minimize the respective corresponding linear risk functions. The proposed model is a discrete, fractional programming problem that is solved using a specialized branch-and-bound approach. A numerical example is provided for the sake of illustration, and some computational experience on randomly generated test cases is provided to study the effort required to solve this problem in different instances. The model is also tested using realistic data associated with a case concerned with routing hazardous materials through the roadways of Bethlehem, Pennsylvania. Data acquisition as well as algorithmic computational issues are discussed.

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