LAUSR

Staff scheduling involves assigning people to tasks organized in working shifts. It is a complex and time-consuming activity common to several real-world companies while still typically a hand-made task. These problems are usually conditioned by legal and working rules, and by personal preferences. Thus, the challenge is to find schedules that most accurately fit the functionality of the services and equity issues. For this purpose, a column generation-based diving heuristic is proposed to solve a staff scheduling problem at an Emergency Medical Service. The approach is generic and possibly adjusted to several realities and companies. In this context, the heuristic is applied to a real-life problem at Instituto Nacional de Emergencia Medica (INEM), obtaining good quality solutions in relatively short running times. The best-found solution is compared with an implemented schedule at INEM, strengthening the practical value of this approach. The ultimate goal is to develop automated tools to support INEM in their staff scheduling activities.