Risk-based GIS model for hazardous materials routing in Sharjah, United Arab Emirates as a case study

Fairness in hazmat routing-scheduling: A bi-objective Stackelberg game

“…Enhancing and integrating technology (Huang et al, 2004;DeLorenzo et al, 2005), regulating rules (Gordon and Young, 2015) and optimizing routing-and scheduling decisions are various approaches that may improve transportation safety and security. Hazmat routing and routing-scheduling problems have a fairly rich literature (refer to Huang et al, 2004;Bell, 2006;Carotenuto et al, 2007a;Bersani et al, 2010;Kazantzi et al, 2011;Li and Leung, 2011;Xiang et al, 2012;Li et al, 2013;Kang et al, 2014;Mahmoudabadi and Seyedhosseini, 2014;Mishra et al, 2014;Rahman et al, 2014;Pamučar et al, 2016;Sahnoon et al, 2016;Sun et al, 2016;Bula et al, 2017;Özceylan et al, 2017;Wang et al, 2017;Yuan et al, 2017;Sun et al, 2018). Since the focus of this research is on hazmat routing-scheduling, in the upcoming subsections we only focus on hazmat routing-scheduling research studies and exclude pure hazmat routing research studies.…”

Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

“…Mohammadi et al [7] designed a reliable hazardous materials transportation network based on four factors: distance from the incident source, number of people exposed to the risk, number of hazardous materials shipments passing that segment, and the characteristics of the road type. With the rapid development of Geographic Information System (GIS) technology, it has also been applied to risk assessment for hazardous materials transportation (Bubbico et al [8], Sahnoon et al [9], Chen et al [10]). Visual map display and data analysis functions provide convenience and improve accuracy for risk assessment during the transportation of hazardous materials.…”