Strategies for Disaster Preparedness and Disruption Risk Mitigation

The consequences of a disaster can be devastating in the absence of an effective disaster response. However, being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. Likewise, disruptions caused by disasters or other emergencies can have an enormous economic impact on supply chains unless an adequate mitigation strategy is implemented. In this dissertation, we study the strategies for disaster preparedness and disruption risk mitigation. Prepositioning emergency relief items in emergency response facilities before an anticipated disaster is a common strategy to increase the effectiveness of relief distribution. Relief distribution activities are hampered due to damaged roads, which can be restored by repair teams using restoration equipment. We propose a two-stage stochastic programming model integrating facility location and network restoration decisions. Our integrated model decides on the location of restoration equipment prior to the disaster in addition to the facility location decisions. Moreover, decisions related to relief item distribution and network restoration are made jointly after the disaster when demand uncertainty, supply availability uncertainty, and network availability uncertainty are resolved. We develop a sample average approximation method with concentration sets motivated by Rosing and ReVelle (1997)’s Heuristic Concentration to solve our integrated model efficiently. These concentration sets are comprised of promising locations identified by information obtained from disaster scenarios. We use our heuristic solution approach to generate a preparedness strategy for an anticipated earthquake in Istanbul. Our computational results show that limiting the solution space in the first stage to concentration sets reduce the problem size without sacrificing the solution quality significantly. Our results also show significant improvement in unmet demand and cost measures by integrating location and network restoration models. We also develop an exact solution approach based on exploring the first-stage solution space on a branch-and-bound tree and decomposing the second-stage problem into subproblems for fixed first-stage solutions. Optimality cuts derived from these subproblems remove non-optimal solutions from further consideration. We use the integer optimality cuts, which are employed in the integer L-shaped method by Laporte and Louveaux (1993), since our problem includes binary decision variables in the second stage. Our approach mainly follows an improved version of the integer L-shaped method by Angulo, Ahmed, and Dey (2016) with two modifications to better exploit the structure of our integrated model. Our computational results show that the solution time of our algorithm increases only linearly as the number of scenarios increases. The economic impact of disruptions can be enormous especially when the supply chain design is tightly optimized to perform well under normal circumstances. Driven by just-in-time philosophy, many companies are reluctant to invest in risk mitigation even though supply chain reliability can be improved significantly by small additional investments. We develop a decision support framework based on a multistage stochastic programming model to choose the best mitigation strategy against the risk that a tier-1 supplier cannot supply parts. We propose a holistic approach which considers a variety of feasible strategies including improving the reliability of the primary supplier through backup capacity, reserving capacity from a secondary supplier, and increasing inventory levels. Our results show that the payoff of investing in mitigation strategies can be large if a disruption occurs, in particular, when the adverse effects of the disruption cannot be eliminated for a long time.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/151487/1/ecesanci_1.pd