Modelling the logisitics response to disasters
This thesis is devoted to optimize the health care logistics which can support emergency management plans to reduce the impacts of natural and/or man-made disasters. After the review of relevant papers, two main gaps have been found in the current studies. One is that most of the researches are not based on real cases. The other is that some main characteristics of disasters are neglected when disasters are studied. Therefore, based on real case scenarios, the thesis studies different disasters (natural and/or man-made disasters) separately according to the characteristics of disasters. Natural disasters may be predicted but are difficult to avoid. Therefore, the evacuation of potential victims and the dimensioning of relief resources are crucially important. A three-step approach is proposed to study the resource dimensioning and the organization of emergency management plan (French White Plan) facing natural disasters. In our three-step approach, the first step builds a framework model to get the insights of emergency management plan clearly. The second step establishes a global model (a linear model) to predict the quantity of required resources for evacuation. The third step proposes a detailed simulation model to reflect the real world more precisely. The hospital evacuation under the guidance of a French Extended White Plan in case of a flood has been taken as a real case scenario to test the correctness of our approach. The man-made disasters and the outbreak of diseases can be large-scale disasters which require a high demand of resources. In this thesis, a model for logistics response to bioterrorist attack with a non-contagious agent and another model for the logistics response to epidemics have been proposed. Multi-period and multi-echelon inventory management problems have been studied. The two models (a linear model and a non linear model respectively) combine the main characteristics of disasters: the propagation of the disease, the relevant medical interventions and the logistics deployment together. The number of patients in different disease stages and the required medical resources for each period can be estimated. The factors affecting the number of deaths and the different medical intervention policies can also be evaluated with the two models. With the help of the models, the decision makers can get an idea of the disaster situation and the relevant medical responses from a strategy level. A logistics response to an anonymous bioterrorist attack with anthrax to a shopping center and the logistics response to the outbreak of H5N1 are taken as real case scenarios to test the effectiveness of the models respectively.