Surgery scheduling must balance capacity utilization and demand so that the arrival rate does not exceed the effective production rate. However, authorized overtime increases because of random patient arrivals and cycle times. This paper proposes an algorithm that allows the estimation of the mean effective process time and the coefficient of variation. The algorithm quantifies patient flow variability. When the parameters are identified, takt time approach gives a solution that minimizes the variability in production rates and workload, as mentioned in the literature. However, this approach has limitations for the problem of a flow shop with an unbalanced, highly variable cycle time process. The main contribution of the paper is to develop a method called takt time, which is based on group technology. A simulation model is combined with the case study, and the capacity buffers are optimized against the remaining variability for each group. The proposed methodology results in a decrease in the waiting time for each operating room from 46 minutes to 5 minutes and a decrease in overtime from 139 minutes to 75 minutes, which represents an improvement of 89% and 46%, respectively.
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