Supply chain disruptions not only impact the regular operations, but can also affect the reputation of an organization. In this thesis, we considered a capacitated network that transported products from the source node to the sink node based on an operating plan. This network underwent a disruption, which led to arc closures. The problem of interest is to move the products from the source to the sink with minimal deviation from the original operating plan, and also transport a sufficiently large amount of the products. We proposed optimization models to minimize the dissimilarity between two operating plans and also transport a sufficient value of flow to the sink. These models are motivated by lexicographic goal programming philosophy. Further, we implemented these models to understand the scalability of these models. The solver parameters were tuned to enhance the computational performance of our models. We also developed a visualization aid for the end-user. The visualization was designed to help the user understand the merits of our models. We also conducted a visualization experiment to understand the impact of number of disrupted arcs on dissimilarity.