Rassal bir sonraki gün ameliyathane planlamasında kullanılacak parametrik ve non parametrik modeller

Operating rooms are the resources that generate the most part of the revenue of hospitals. On the other hand, they generate the most part of the expenses, as well. Because of the uncertainty of surgery durations, scheduling operating rooms are very difficult. But their impact on the finances of a hospital makes it vital for the planners to carry out scheduling as best as they can. Another problem that lies in the way of fine operating room scheduling is limited surgery data available for use. Uncertainty and diversity of surgeries that may take place in a given operating room makes it difficult to obtain sufficient amount of surgery duration data. In this study we describe a stochastic optimization model for computing OR schedules that are effected by the uncertainty in surgery durations. We focus on scheduling start times. We show that our model can be used to generate substantial reductions in OR team waiting, OR idling, overtime costs. The model in this study is studied with 3 solution approaches: (i) parametric approach, (ii) non parametric approach, (iii) a simple but practical heuristic. Considering all scenarios in this study, parametric approach manages to perform 6,18% close to optimal solution, whereas non parametric approach performs 7,66% and heuristic approach performs 78,17% close to optimal solution. When compared to non parametric approach, parametric approach performs better when number of historical surgery duration sample size is small. In contrast, when the number of historical surgery duration sample size is large, non parametric approach starts performing better. All three solution approaches provide meaningful results, where parametric approach performs better in most cases when compared to other solution approaches.