Contributions in supply chain risk assessment and mitigation
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This dissertation develops contributions in the area of supply chain risk assessment and mitigation. In each of the three main chapters, we present and analyze a risk assessment or mitigation problem for supply chains. The first problem is to assess the impact of infrastructure disruptions on supply chain performance; the second problem is to develop an operational control approach to mitigate risks posed by uncertain events that disrupt network synchronization; and the third problem is to analyze the risk posed by an adversary seeking to use a supply chain as a weapon. Chapter II presents a methodology for assessing the excess supply chain costs that arise from a failure of or an attack on a critical supply chain infrastructure component. Different from many subjective risk assessment practices, our methodology provides a systematic approach to search for the most vulnerable supply chain components and measure the economic consequences of disruption. Modeling a supply chain using network flow models, we analyze the impact of disruption by linear programming theory, and propose an efficient assessment algorithm based on the dual network simplex method. Finally, a case study on the U.S. corn export supply chain is presented. Chapter III discusses the mitigation of risks created by transit time uncertainties in less-than-truckload (LTL) line-haul operations. Transit time uncertainty may undermine the performance of the load plan, which specifies the route for each shipment and is synchronized to reduce line-haul costs. In our study, risk assessment of a load plan is performed via a dispatch simulation under randomly generated travel time scenarios. The risk consequence is measured by the average excess operational cost, including transportation cost and handling cost. Compared to existing line-haul network models embedded within integer programming approaches for load plan optimization, the dispatch simulation can evaluate the performance of a load plan more realistically. In addition, a heuristic search algorithm based on "multi-tree pivots" is provided to obtain a cost-efficient load plan that is robust to transit time uncertainties. Chapter IV presents methodology to assess the consequence of risks which arise from the intentional contamination of a food supply chain. Different from many risk management practices, the source of risk in this problem is an intelligent adversary, e.g., a terrorist group, who intends to deliver chemical or biological toxins to consumers using the supply chain. First, a general modeling scheme based on state-space models is provided to describe the dissemination of toxin across consumed products in a food supply chain. Then, a case study based on a representative liquid egg supply chain is presented. Based on the system model, a risk assessment for different supply chain designs is performed by simulation. Moreover, an in-depth analysis is conducted to determine the worst-case consequence given an intelligent attack considering the operational characteristics of the system. The worst-case consequence tool developed is designed to be embedded within any risk assessment approach.