Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation
Document Type
Article
Publication Date
11-2016
Keywords
hazardous materials transportation, conditional value-at-risk, robust optimization
Digital Object Identifier (DOI)
https://doi.org/10.1287/trsc.2015.0639
Abstract
Despite significant advances in risk management, the routing of hazardous materials (hazmat) has relied on relatively simplistic methods. In this paper, we apply an advanced risk measure, called conditional value-at-risk (CVaR), for routing hazmat trucks. CVaR offers a flexible, risk-averse, and computationally tractable routing method that is appropriate for hazmat accident mitigation strategies. The two important data types in hazmat transportation are accident probabilities and accident consequences, both of which are subject to many ambiguous factors. In addition, historical data are usually insufficient to construct a probability distribution of accident probabilities and consequences. This motivates our development of a new robust optimization approach for considering the worst-case CVaR (WCVaR) under data uncertainty. We study important axioms to ensure that both the CVaR and WCVaR risk measures are coherent and appropriate in the context of hazmat transportation. We also devise a computational method for WCVaR and demonstrate the proposed WCVaR concept with a case study in a realistic road network.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Transportation Science, v. 50, issue 4, p. 1174-1187
Scholar Commons Citation
Toumazis, Iakovos and Kwon, Changhyun, "Worst-Case Conditional Value-at-Risk Minimization for Hazardous Materials Transportation" (2016). Industrial and Management Systems Engineering Faculty Publications. 3.
https://digitalcommons.usf.edu/egs_facpub/3
