Integrated robust scheduling for inpatient surgery and ambulatory surgery

Abstract

Ambulatory surgery services are becoming more and more popular as they can alleviate the shortage of hospital bed resources and increase the number of surgeries without increasing additional investment. In this mode, ambulatory surgery and inpatient surgery have different operating rooms but the same surgeons, which makes it difficult to coordinate the operating rooms and surgery time of the surgeon. In addition, the uncertainty of surgery duration and disruptions aggravate the difficulty of scheduling. Therefore, we model an integrated scheduling problem of ambulatory surgery and inpatient surgery under the in-hospital ambulatory surgery services mode. Considering the uncertainty of surgery duration and disruptions, a disruption management model based on distributionally robust optimization and machine learning consensus is established. For the computational complexity of the problem, we transform it into a two-stage robust problem and propose a disruption management algorithm to solve it. The experiment proves that integrated scheduling considering ambulatory surgery and inpatient surgery can significantly reduce the cost associated with the ORs and improve the utilization of the ORs. In addition, disruption management is not a once-and-for-all exercise, and the occurrence of new disruptions needs to be taken into account when performing disruption management.