Monte Carlo Sensitivity Analysis Of Unknown Parameters In Hazardous Materials Transportation Risk Assessment

The US Department of Transportation was interested in the risks associated with transporting Hydrazine in tanks with and without relief devices. Hydrazine is both highly toxic and flammable, as well as corrosive. Consequently, there was a conflict as to whether a relief device should be used or not. Data were not available on the impact of relief devices on release probabilities or the impact of Hydrazine on the likelihood of fires and explosions. In this paper, a Monte Carlo sensitivity analysis of the unknown parameters was used to assess the risks associated with highway transport of Hydrazine. To help determine whether or not relief devices should be used, fault trees and event trees were used to model the sequences of events that could lead to adverse consequences during transport of Hydrazine. The event probabilities in the event trees were derived as functions of the parameters whose effects were not known. The impacts of these parameters on the risk of toxic exposures, fires, and explosions were analyzed through a Monte Carlo sensitivity analysis and analyzed statistically through an analysis of variance. The analysis allowed the determination of which of the unknown parameters had a significant impact on the risks. It also provided the necessary support to a critical transportation decision even though the values of several key parameters were not known

Understanding simulation solutions to resource constrained project scheduling problems with stochastic task durations

The project scheduling problem domain is an important research and applications area of engineering management. Recently introduced project scheduling software such as Risk+, @Risk for Project, SCRAM and Risk Master have facilitated the use of simulation to solve project scheduling problems with stochastic task durations. Practitioners, however, should be made aware that the solution algorithm used in these software systems is based on the implicit assumption of perfect information, an assumption that jeopardizes the feasibility of solution results. This paper discusses the impact of assuming perfect information, introduces a multi-period stochastic programming based model of the project scheduling problem with stochastic task durations, and presents an alternative simulation algorithm that does not assume the availability of perfect information. A simple case study is used to illustrate the practical implications of applying simulation to address project scheduling problems with stochastic task durations. © Taylor & Francis