Le 17 décembre 2018, à 9h, salle des thèses de la faculté Jean Perrin.

Résumé :

In this thesis, we focus on the routing and scheduling activities of patients in the Healthcare Emergency Department while involving optimization tools. We address three categories of research questions. The first category includes question about patient flow to the Emergency department. In this part we focuses on patient transportation problem derived from emergency medical services (EMS). Ambulance routing problem is a significant challenge. Bearing in mind that the travel cost of an EMS is a crucial feature since cost is vital in emergency situations. Hence, we aim to push forward the total travel cost performance of EMS by handling the ARP. In order to do so, we propose a cluster-first route-second algorithm based on the Petal algorithm and the particle swarm optimization approach. In order to study the performance of the proposed method, we test it in a set of instances and compared the results with state-of-art methods. The second category is associated to the patient flow in the Emergency Department. A mixed integer linear programming, a hybrid ILS/VND method, a genetic algorithm and a reactive algorithm are developed to solve this real patient scheduling problem. Approximate methods are alternatives to exact methods to quickly solve large instances taking into account a set of constraints. The reactive algorithm consist to schedule patients with consideration of perturbation. The third category of questions is a combination of two previous questions. It deals with routing and scheduling process of patients where we investigate the patient flow to and in the ED. A Decision Support System was set up to facilitate mutual interaction and cooperation between the optimization tools and the Decision Maker in the addressed case study.