Operating Room Pooling and Parallel Surgery Processing Under Uncertainty

Abstract

O perating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. We use this model to quantify the benefit of pooling ORs as a shared resource and to illustrate the impact of parallel surgery processing on surgery schedules. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realistic-sized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the impact of parallel surgery processing and the benefit of OR pooling are significant. The latter may lead to total cost reductions between 21% and 59% on average.

Integrated decision making in healthcare : an operations research and management science perspective

“…Dates and operating rooms are assigned to the elective cases on the surgical waiting list, following the settled admission control decisions [23,175,176,235,274,341,416]. The available blocks of operating time capacity are filled with elective cases.…”

“…Factors to consider in the sequencing decision are doctor preference [224], medical or safety reasons [81,274], patient convenience [81,82], and resource restrictions [83]. Various rules for sequencing surgical cases are known [23,81,82,226,398,416,444]. In general, the traditional firstcome-first-serve (FCFS) rule is outperformed by a longest-processing-timefirst (LPTF) rule [49,300,302,387].…”

“…This provides a target time for planning the presurgical and postsurgical resources, and for planning the doctor schedules [511]. The actual start time of a surgical case is impacted by the planned and actual duration of all preceding surgical cases [23,511] and the completion time of the preoperative stage [145]. Emergency cases may play a significant role during the execution of the surgical case schedule [224].…”

“…Methods: computer simulation [10,57,102,137,140,142,143,146,170,226,300,302,306,307,433,470,511], heuristics [10,13,83,131,136,175,177,225,226,256,306,308,341,416,422,455,486], Markov processes [196,228,365,381], mathematical programming [13,23,81,82,83,95,102,129,130,131,175,176,177,…”

“…Methods: computer simulation [281,300,301,302,334,432,433,490], heuristics [72,130,256], mathematical programming [23,72,130,469], queueing theory [334], literature review [282,351].…”