Workshift Scheduling Using Optimization and Process Mining Techniques: An Application in Healthcare

This paper aims at supporting healthcare organizations in automatically generating rostering plans by combining optimization and process mining approaches. Based on event logs from the information system, we propose a decision support system that simulates work schedules. Managing staff workshifts is a complicated issue to solve especially in large and complex organizations such as those in the healthcare sector. A number of different factors can be taken into account, i.e., operative constraints, personal preferences and regulations must be considered in order to produce the best plan. In our approach we exploit the idea for which the patterns included in the realised rostering plans could represent the personal needs and the unspoken habits of the personnel. Based on this remark, we propose a three-step methodological framework - rostering optimization, pattern extraction, pattern adaptation - that it was applied to a real-world scenario.