Bounding the Likelihood of Exceeding Ward Capacity in Stochastic Surgery Scheduling

The stochastic high-patient-throughput surgery scheduling problem under a limited number of staffed ward beds is addressed in this paper. This work proposes novel way to minimize the risk last-minute cancellations by bounding likelihood exceeding beds. Given historical data, it possible determine an empirical distribution for length stay ward. Then, any given combinations patients, one can estimate using Monte Carlo sampling. As these patient grow exponentially, alternative, more efficient, worst-case robust optimization model compared. An extensive data set was collected from National University Hospital Iceland computational experiments, and models were compared with actual data. proposed achieve high quality solutions terms overtime overflow