Abstract No: 025-0701 Title: A Genetic Algorithm Approach for Nurse Scheduling in an Operating suite

No: 025-0701 Title: A Genetic Algorithm Approach for Nurse Scheduling in an Operating suite Elham Torabi Department of Operations, Business Analytics and Information Systems 534 Carl H. Lindner College of Business University of Cincinnati 2925 Campus Green Dr. Cincinnati OH 45221 [email protected], (513) 288-2546 Gino J. Lim Department of Industrial Engineering University of Houston E211 Engineering Building 2, Houston, TX 77204 [email protected], (713) 743-4194 Craig M. Froehle Department of Operations, Business Analytics and Information Systems 521 Carl H. Lindner College of Business University of Cincinnati 2925 Campus Green Dr. Cincinnati OH 45221 [email protected] , (513) 556-7174 POMS 23rd Annual Conference Chicago, Illinois, U.S.A. April 20 to April 23, 2011 A Genetic Algorithm Approach for Nurse Assignment Problem in an Operating suite Abstract In this paper we present a Genetic Algorithm approach for scheduling operating room (OR) nurses. Most studies in operating room scheduling deal with patient flow analysis and physician scheduling, limited literature has focused on scheduling OR nurses. Our objective is to minimize nurses’ idle time, overtime and non-consecutive assignments during overtime hours while maximizing demand satisfaction. The major constraints are: 1) shift constraints and 2) match between nurses’ skill sets and surgery requirements. Due to the large size of the problem, finding an optimal solution is extremely difficult. Therefore, a Genetic Algorithms approach is proposed to find a set of good schedules in a reasonable amount of time. The solutions were then evaluated in a simulation model with probabilistic surgery durations. The best performing solution in the stochastic environment is selected as the final schedule. We present a set of case studies to demonstrate the performance of our approach. We used sample data gathered from a large urban teaching hospital that has 31 operating rooms.In this paper we present a Genetic Algorithm approach for scheduling operating room (OR) nurses. Most studies in operating room scheduling deal with patient flow analysis and physician scheduling, limited literature has focused on scheduling OR nurses. Our objective is to minimize nurses’ idle time, overtime and non-consecutive assignments during overtime hours while maximizing demand satisfaction. The major constraints are: 1) shift constraints and 2) match between nurses’ skill sets and surgery requirements. Due to the large size of the problem, finding an optimal solution is extremely difficult. Therefore, a Genetic Algorithms approach is proposed to find a set of good schedules in a reasonable amount of time. The solutions were then evaluated in a simulation model with probabilistic surgery durations. The best performing solution in the stochastic environment is selected as the final schedule. We present a set of case studies to demonstrate the performance of our approach. We used sample data gathered from a large urban teaching hospital that has 31 operating rooms.