چكيده به لاتين
Today, the most effective use of human resources, which is the most expensive resource for most organizations, is very important. In this study among the existing organizations facing this challenge, the hospital as large Health unit was considered. On the other hand, given that the cost of paying nurses is a large part of the hospital Cost, as well as a shortage of nurses is a serious problem. Hence, administrators of this system have to use decision making and optimization tools to improve the efficiency of health systems and provide quick access to services. After extensive literature review in this field and reviewing the conditions of previous studies and its adaptation to the problem in this study, an integer multi-objective mathematical model was used for the hospital nurses scheduling problem. The purpose of the model is to maximize the preferences and cohesion between the nursing team and deviation from soft constraints, so that all demands of the job shifts were met. Nurse preferences were calculated using the preference rating and the data of the preferences of past scheduling periods and nursing service history for the benefit of the nurses using the Data Envelopment Analysis(DEA). The paired comparison was used to calculate the deviation weight of soft constraints. A new pattern was used to assign nurses to work shifts. In this pattern, for nurses' coordination, instead of overlapping shifts for half an hour, two nurses with a time delay of one hour in each other appear in shifts. Also, using the robust optimization approach, the minimum and maximum hours of nurses' work were considered uncertain. As a case study, data from the ICU unit of Loghman Hakim Hospital in Tehran was considered. The results of implemanting the proposed model to the nursing scheduling problem in a case study show a significant improvement in terms of the time schedule. The scheduler has increased the response to nurse preferences and continuity between the nursing team. Also, the use of a two-group approach has reduced the costs of nurses in the hospital.
