The transport of hazardous materials is an important strategic and tactical decision problem. Risks associated with this activity make transport planning difficult. Although most existing analytical approaches for hazardous materials transport account for risk, there is no agreement among researchers on how to model the associated risks. This paper provides an overview of the prevailing models, and addresses the question “Does it matter how we quantify transport risk?” Our empirical analysis on the U.S. road network suggests that different risk models usually select different “optimal” paths for a hazmat shipment between a given origin-destination pair. Furthermore, the optimal path for one model could perform very poorly under another model. This suggests that researchers and practitioners must pay considerable attention to the modeling of risks in hazardous materials transport.

D angerous-goods shipments remain regulated despite the widespread deregulation of the transportation industry. This is mainly due to the societal and environmental risks associated with these shipments. One of the common tools used by governments in mitigating transport risk is to close certain roads to vehicles carrying hazardous materials. In effect, the road network available to dangerous goods carriers can be determined by the government. The associated transport risk, however, is determined by the carriers' route choices. We provide a bilevel programming formulation for this network design problem. Our approach is unique in terms of its focus on the nature of the relationship between the regulator and carriers. We present an application of our methodology in Western Ontario, Canada.

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An increasing number of companies have been implementing comprehensive recycling and remanufacturing programs. These endeavors typically involve the operation of joint manufacturing and remanufacturing systems. One of the major challenges in managing such hybrid systems is the stochastic nature of product returns. In particular, there is significant variability in the condition of the returns. This paper presents an approach for assessing the impact of quality-based categorization of returned products. Through extensive numerical studies on a continuous-time Markov chain model, we show that incorporation of returned product quality in the remanufacturing and disposal decisions can lead to significant cost savings. We find that these savings are amplified as the return quality decreases, and as the return rate increases. We also show that prioritizing higher quality returns in remanufacturing is, in general, a better strategy.