New nurse scheduling problem considering burnout factor and undesirable shifts under COVID-19 (A real case study)

In recent years, the outbreak of COVID-19 has led to burnout of healthcare personnel. Accordingly, more attention should be paid to nurses scheduling and their preferences. The Nurse Scheduling Problem (NSP) as an optimization concept provides suitable nurses' schedules by focusing on the system requirements. In this study, a new NSP is developed in which the factors and consequences of nurses' burnout are considered simultaneously. In the proposed model, new constraints are formulated to define the undesirable shifts. Due to the seniority rules, it is tried to restrict the numeral of these shifts in the generated timetable to improve the burnout of nurses. In addition, an attempt is made to fairly allocate the requested leave of nurses by considering their leave days during the previous horizons. In the presented model, the timetable of nurses is flexible to cope with the absence of employees, and the required personnel are covered by changing shifts among nurses. To solve the developed problem, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are coded, and their results are compared with GAMS in different test problems. Taguchi method has been applied for parameter tuning of these algorithms. The final results prove that the GA outperforms PSO both in obtained solution and CPU time. GA has only a 0.22 % optimality gap on average. Finally, the proposed model is implemented in an actual case study in Iran. The generated timetable improves nurses' performance and the level of medical services by controlling the causes and consequences of burnout.