Please use this identifier to cite or link to this item:
http://hdl.handle.net/11718/21453
Title: | An infrastructure investment methodology to risk mitigation from rail hazmat shipments |
Authors: | Verma, Manish |
Keywords: | Hazmat;Investment methodology;Infrastructure investment;Risk mitigation |
Issue Date: | 25-Jan-2019 |
Publisher: | Indian Institute of Management Ahmedabad |
Abstract: | A significant majority of hazardous materials (hazmat), including those integral to sustain our industrial lifestyle such as crude oil, are produced and consumed at different locations, and therefore need to be transported over long distances. In North America, a significant portion of hazmat shipments is transported over the railroad network. Although railroad is one of the safest modes for hazmat transport, the possibility of catastrophic events resulting from multi railcar incidents does exist. The tragic event in Lac-Megantic (Quebec, Canada), in July 2013, is a reminder of the possible catastrophe associated with rai hazmat shipments. We propose a novel methodology to mitigate risk from rail hazmat shipments. The proposed methodology entails ascertaining the riskiest locations in the current railroad network, which are then designated as candidates for potential investment decisions. Finally, a bi-level optimization program is solved to determine optimum investment solutions. Computational experiments on the railroad network in Canada demonstrate significant risk-reduction is alternative rail-links are built around population centers, and also show that risk-reduction function is non-linear with non-monotonous behavior. We conclude by an outline about possible future research directions. |
URI: | http://hdl.handle.net/11718/21453 |
Appears in Collections: | R & P Seminar |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RP_January_25_2019.html | RP_January_25_2019 | 960 B | HTML | View/Open |
Items in IIMA Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated.