Healthcare network design and inventory decisions for disaster management under uncertainty

Large-scale disasters have occurred in many parts of the world in recent years. A large number of victims suffer from these disasters. The survival of victims after the disaster is largely based on humanitarian assistance. Focusing on this humanitarian assistance, firstly, we consider healthcare location decisions for providing efficient service in a disaster situation and mobile hospitals can be utilized for this purpose. We introduce a network design model considering the location and re-location decisions of mobile hospitals using a two-stage stochastic programming model with various disaster scenarios. We apply our models to a real-life case study for an expected earthquake in Istanbul and extract several managerial insights. Numerical experiments demonstrate that mobile hospitals can provide significant improvements in healthcare service. Secondly, we aim to decide on the amount of relief aid material to procure before and after the disaster considering the budget limitations and focus on how to allocate the budget among different materials. For this purpose, we first develop single product models with and without budget constraints. Then, we build a two-stage stochastic model with budget constraints for the two-product case and determine its optimal solution. Then, we extend our analysis for multiple product cases and propose approximation algorithms. We compare the results of the models via detailed numerical studies and extract managerial insights for disaster preparedness. Finally, in the last part of this thesis, we focus on the multi-period stocking decisions in case of a disaster. We present a dynamic programming model to decide on the optimal stocking and ordering policy considering the budget limitations, random timings, quantities of donations, random demand for materials. The managers need to make a decision at any point in time considering the available budget and available stock at that time. Due to the dynamic nature of this system, the budget and the stock changes over time because of arriving donations and usage of materials during disasters. We develop dynamic programming models for this system and extract managerial insights through the solutions of these models.