Abstract:
In this thesis we are interested in two distinct problems within the disaster management context. These problems are modeled by two-stage stochastic integer programming since stochasticity is inherent in natural disasters. First, we consider a humanitarian relief logistics model which can be used as a pre-disaster planning tool that also considers post-disaster decisions to give an effective response aftermath an earthquake. In this model, decisions are made for location of pre- and post-disaster rescue centers, the amount of relief items to be stocked at the pre-disaster rescue centers, the amount of relief item flows at each echelon, and the amount of relief item shortage. The objective is to minimize the total cost of facility location, inventory holding, transportation and shortage. Since the building and transportation network retrofitting decisions affect the pre-disaster planning of post-disaster response decisions, we propose an integrated model that includes these retrofitting decisions as well. The total mitigation budget is allocated among these mitigation alternatives. The amount of relief item demand is a decision variable that is determined according to the postdisaster damage of buildings. The objective function is defined as the minimization of the total cost of retrofitting, transportation and shortage of relief item demand. The deterministic equivalents of both models are formulated as mixed-integer linear programming models (MILP) and solved by Lagrangean heuristic methods. Results on randomly generated test instances show that the proposed solution methods for both models exhibit good performance under different parameter settings. Also, the value of stochastic solution for both models are high, which validates the incorporation of the uncertainty in the proposed models. In addition, for the integrated model, various analyses are carried out to clearly understand the model behaviour.
