Data driven route scheduling for mobile nursing services

TrAmP

The demographic change in Germany leads to an increasingly elder population which is accompanied by growing needs for stationary and ambulant care and nursing services. Under normal conditions, the elderly are cared for either in stationary nursing homes or via a mobile nursing service, that is providing different services for several patients at different places over the day. These mobile nursing services have the advantage of enabling the patients to stay and live in their used and loved environment while still providing professional care and support.

The recent state of route scheduling for most nursing services is a manual planning procedure which has to consider several side conditions. These conditions are e.g. the qualification of the service employee to inject specific medicine or to handle particular patient requirements. Furthermore vacation and sickness of employees as well as over time schedules have to be considered.

The project team consisting of the Information Systems and Machine Learning Lab (ISMLL) of the University of Hildesheim and the MediFox GmbH, the leading software provider for software solutions for mobile nursing services plans to improve the route scheduling capabilities of existing software applications. Therefore we will develop a software prototype which adopts state-of-the art heuristic optimization methods to find a ideal route and operation schedule considering all side conditions.

In a second step a smart optimization model is developed, which learns how to automatically optimize respective arbitrary routing problems by employing meta learning approaches, facilitated by the huge data base of MediFox.

Partner: MediFox GmbH

Contact:
Lars Schmidt-Thieme
Jonas Falkner