INVESTIGATING UNCERTAINTIES IN NURSE SCHEDULING PROBLEM USING FUZZY MATHEMATICAL MODELING APPROACH

Nowadays, the health systems consume a significant share of the budget in each country. Hospitals are considered as the largest organizations to provide the healthcare services. Nurses as one of the major portion of hospitals' human resources consume a considerable part of the hospitals' annual budget. From this point of view, the hospitals' managers must arrange the available nurses efficiently. This problem is worsened by the shortage of the available nurses in many countries. In this setting, the Nurse Scheduling Problem (NSP) has received significant attention in recent years. In this problem, the aim is to assign the shifts to the nurses by satisfying the requirements during the planning horizon. Several factors such as hospital managers' policies, labor laws, governmental regulation, and the status of the nurses at the end of the previous planning horizon should be considered for assigning the shifts to the nurses. Several mathematical models and heuristic and meta-heuristic algorithms have been proposed to solve the NSP by considering various assumptions and constraints. In the real-world problems, the good quality solutions can be obtained by considering the uncertainty concepts in the research problem. In this point of view, in the current study, the uncertainty concepts are considered in the nurse scheduling problem. It can be stated that the nurses' preferences for the working shifts cannot be deterministically specified. For this reason, first, a mathematical programming model is developed to maximize the nurses' preferences to work in their favorable shifts. Then, a fuzzy mathematical modeling approach based on the averaging fuzzy operator is proposed to investigate the uncertainty concepts on the nurses' preferences for the working shifts. Then, some random test problems are generated and solved to evaluate the performance of the developed fuzzy model. Regarding the obtained results, it can be stated that high quality schedules are generated by the proposed fuzzy model.