A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study

Nurses’ scheduling problems have attracted a significant amount of healthcare research, indicating the importance of these issues. In this paper, it has tried to present a multi-objective model for the assignment of nurses and anesthesiologists to surgical teams, considering frequency and fairness in allocating time to staff members. Since idle time is inevitable, we seek to divide idle time equally among staff. In addition, the break time of each staff member have an almost regular frequency during the shift. Minimizing overtime costs and maximizing attention to the willingness of surgical staff members to work overtime are other objectives of the problem. Three metaheuristic algorithms NSGA-II, MOPSO, and SPEA-II used to solve the presented model. A hybrid multi-objective genetic algorithm based on variable neighborhood search is also presented. The comparison of the solutions of 4 algorithms shows that the proposed hybrid algorithm has a significant superiority compared to other algorithms in terms of the average value of the solution, the quality of the Pareto solution set, and execution time. The presented model is compared with the real data of the surgical department of elective patients of a government hospital in Qazvin province. The obtained results show that the presented model has significantly created equality in the amount of working time of nurses and anesthesiologists in the elective surgery department. It has also spread the idle time of each staff member during the work shift, which has caused different time breaks for each one.