Abstract

In this research, we study a stochastic operating room planning problem with the possibility of restricted pooling of downstream beds among different specialties. Here, we suppose that there is a limited number of beds that can be shared among specialties. In this problem, surgical durations and patients’ length of stay are stochastic. We developed a two-stage stochastic integer programming model, where in the first-stage we decide on 1) the number of ICU and wards beds to be allocated to each specialty, and 2) the allocation of surgeries to operating rooms during the planning horizon. In the second stage, we decide on 1) how many shared beds in ICU and wards are allocated to which specialties on each day during the planning horizon, 2) the surge capacity required to satisfy downstream service to patients, and 3) the overtime incurred in each operating room during the planning horizon. The proposed model aims at minimizing the total cost including the patients’ waiting cost, postpone cost, overtime and fixed cost of operating rooms, and the cost of downstream surge capacity.
We have implemented the proposed stochastic programing model in a sample average approximation framework. We have carried out extensive computational experiments to evaluate the effectiveness of several pooling policies for downstream beds and also the efficiency of the proposed sample average approximation algorithm. We have also performed an extensive sensitivity analysis of cost and the stochastic parameters to provide managerial insights. Our results demonstrated that the sharing policy among different specialties in the downstream units enhance the functionality of the system up to 19.53%. Moreover, the results indicated that the solutions obtained by proposed stochastic model outperforms the solutions from the corresponding deterministic problem by 17.43% on average.