Vasiliki Kazantzi
Technological Education Institute of Larissa, Greece
Nikolas Kazantzis
Worcester Polytechnic Institute, Worcester, MA, USA
Vassilis Gerogiannis
Technological Education Institute of Larissa, Greece
The present paper explores the effects of different risk incidents on a Transportation Model developed for HazMat (Hazardous Materials) shipments. The particular objective of this study is to elucidate the effects of occurrence probabilities of the different risk events on the transportation model featuring total transportation cost. First, the present research study addresses the problem of identifying and evaluating various risk factors that influence the HazMat transportation network. Next, a modeling framework for HazMat transportation is proposed as a special case of the traditional transportation network in the presence of risk-informed safety constraints that need to be satisfied. A comprehensive characterization of the underlying risk profile that is probabilistically realized through appropriately conducted Monte Carlo simulations that capture the effect of underlying irreducible uncertainties associated with the main risk-drivers is followed by the proposed solution approach to the corresponding minimum cost flow network problem while reducing risks at the desired levels. In order to examine the impact of the occurrence possibilities of different risk events on the transportation model, appropriate parameterized simulation work is carried out, resulting in identifying release probability zones and safe network configurations that correspond to certain low-risk levels and degrees of risk-related uncertainties. These simulation studies help making informed decisions on optimal transportation configurations for ensuring safety in hazardous material shipments. Finally, the presented methodological framework demonstrates its potential usefulness in making risk-informed decisions while transporting goods and classes of materials that can be potentially proven dangerous to public health and ecosystem functions.
This paper has been downloaded 2045 times since published. The persistent DOI of this paper is DOI:10.31387/oscm0100067.