Dynamic Vehicle Routing Problem for Medical Emergency Management

Nowadays telemedicine applications are more and more present in the state-of-the-art medicine. Telemedicine is a good way to improve access to healthcare, quality of care, reduce isolation and also costs. In that way we can now safely perform surgery between two places separated by several thousand km, navigate in 3D models of blood vessels or generate 3D models from Nuclear-Magnetic Resonance Imaging (MRI). But there is currently a lack of tools for all day medical acts which could improve medical system efficiency especially for medical emergency services. In order to help medical emergency services, the project MERCURE (Mobile and Network for the Private clinic, the Urgency or the External Residence) has been launched in order to create tools that optimize, follow and manage emergency interventions. The current problem is that the choice of the doctor for a patient is done by hand.The call center is neither aware of the exact location nor the current state of the doctors. Thus it is rarely the best located doctor who is chosen and moreover he may not have correct equipments to heal the patient. To optimize that aspect, we have developed software allowing the optimized management of human and material medical resources. This problem, conventionally called vehicle routing problem (VRP), is one of the most widely studied problems in combinatorial optimization. In the standard VRP, a fleet of vehicles must be routed to visit a set of customers at minimum cost, subject to vehicle capacity constraint and route duration constraint. In the static version of the problem, it is assumed that all customers are known in advance to the planning process. In the case of medical emergency management, it includes some dynamic elements. The information data often tends to be uncertain or even unknown at the time of the planning. It may be the case that patients, driving times or service times, are unknown before the day of operation has begun, but become available in real-time. Due to the recent advances in information and communication technologies, such as geographic information systems (GIS), global positioning systems (GPS) and mobile phones, companies are now able to manage vehicle routes in real-time. Hence, with the increased access to these services, the need for robust real-time optimization procedures will be of critical importance, for small to big distribution companies, whose logistics are based on a high reactivity to the customer demand.