A scheduling model for hospital residents
Journal of Medical Systems
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
A 0-1 goal programming model for nurse scheduling
Computers and Operations Research
A multi-objective programming model for scheduling emergency medicine residents
Computers and Industrial Engineering
A two-stage modeling with genetic algorithms for the nurse scheduling problem
Expert Systems with Applications: An International Journal
A constraint programming-based solution approach for medical resident scheduling problems
Computers and Operations Research
A hierarchical goal programming model for scheduling the outpatient clinics
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Medicine residency is three to seven years of challenging graduate medical training that puts a lot of mental and physiological burden over the residents. Like other surgical branches, anesthesia and reanimation departments provide 24h continuous service and the residents are the main providers of this service. The residents are assigned for on-call shifts during their training, as well as working during the regular day shifts. These schedules must address several considerations like preferences of the residents and coverage requirements of two different locations: the intensive care unit (ICU) and the surgery room (SR). In this study we develop a goal programming (GP) model for scheduling the shifts of the residents in the Anesthesia and Reanimation Department of Bezmialem Vakif University Medical School (BUMS). The rules that must be strictly met, like the number of on-duty shifts or preventing block shifts, are formulated as hard constraints. The preferences of the residents like increasing the number of weekends without shifts and assigning duties on the same night to the same social groups are formulated as soft constraints. The penalties for the deviation from the soft constraints are determined by the analytical hierarchy process (AHP). We are able to solve problems of realistic size to optimality in a few seconds. We showed that the proposed formulation, which the department uses currently, has yielded substantial improvements and much better schedules are created with less effort.