Preventive Healthcare Facility Location Problem Under Customer Perspective And System Perspective

With the high pressure and fast paced life in our country, potential health issues come out one after another, health has become an important factor to improve life quality which is been accepted by more and more people. The prevention sense for disease is gradually enhanced and the medical concept for healthcare is gradually formed. As a result, the requirement for preventive medical is increasing and the construction for preventive healthcare facility can meet this requirement to some extent.Under the research on different kinds of location problems and the characteristic of preventive healthcare facility, this thesis has setup a mathematical model for this location problem based on the optimization objectives and related factor. Beside the thesis has designed the heuristic algorithm to solve the model and provide a feasible option for this location problem and support for the decision maker.Firstly, this thesis has reviewed the research situation for general healthcare facility, emergency service facility and preventive healthcare facility, and summarized the location models and methods.Secondly, this thesis has setup two models for preventive healthcare facility location problems (PHFLP) based on customer perspective and system perspective separately. The customer perspective model assumed that customer accept service of the healthcare facility according to their own willing, which is described as gradually cover in the model, and the objective for this model is to maximize the level of their participation. The thesis has adopt the Genetic Algorithm to solve the model with two different selection strategies and simulate with a real case. The system perspective model assumed that it is determined by the decision-maker which facility customer should go to, and customer in one demand point can go to different facilities to get service which is determined by both the travel time of customer and the queueing time. The objective for this mode is to minimize the time the customers take for service including the arrival time and queueing time. This thesis has used the Differential Evolution Algorithm to solve the second model with two different mutation operators, and simulate with a real case as well. Both models has taken advantage of M/G/1 to describe congestion levels, and use levels to describe the service as well.Last, a real case about the Montreal is applied in the two models. By comparing the results, it can verify the effectiveness of the model and provide suggestions for PHFLP problems.