Operating Room Planning And Scheduling Under Multi-Resource Constraints

There are practical and profound contradictions between existing medical resources,service supply capacity,and the growing needs.Empirical management methods have led to tremendous waste to the already tense medical resources.Operating room(OR)is a bottleneck and the costliest department in hospitals.So its management problems are urgently to be solved.The core issue of OR management is planning and scheduling.Therefore,to improve OR efficiency and reduce costs,while improving satisfaction of patients and health care staff,OR planning and scheduling under multi-resource constraints was designed.Firstly,real surgical data was collected and it was found that durations of different surgeries were log-normal distributed,which is a basis for planning and scheduling.Later,a OR rolling plan model considering multi-resource constraints in reality was established,which was aiming at minimizing the weighted sum of the idle time and overtime of ORs.The surgeries to be performed the next day were determined and the ORs and the surgeons were assigned to surgeries.The genetic algorithm(GA)was used to solve the model,and the validity of the model and the method was proved by multiple experiments.Finally,the uncertainty of surgery duration was considered,and a stochastic optimization model of OR scheduling was established.The sample average approximation(SAA)was used to convert the stochastic model into a deterministic one.Through multiple experiments,it was proved that average overtime of each OR was reducing and tending to be stable with the number of surgeons increasing,which is a discipline for OR management.The contributions of this study are as follows: Surgeries,surgeons,ORs and time were matched each other;GA was applied to solve OR planning with large-scale data;SAA was applied to solve the stochastic problem of duration uncertainty;Managerial insights on the number of ORs open and surgeons to perform are observed.