A Progressive Hedging Approach for Surgery Planning Under Uncertainty

Gül S., Denton B. T., Fowler J. W.

INFORMS JOURNAL ON COMPUTING, vol.27, no.4, pp.755-772, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 4
  • Publication Date: 2015
  • Doi Number: 10.1287/ijoc.2015.0658
  • Journal Name: INFORMS JOURNAL ON COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.755-772
  • Keywords: surgery planning, scheduling, stochastic programming, progressive hedging, heuristics, SCHEDULING PROBLEM, CASE CANCELLATION, ELECTIVE SURGERY, STOCHASTIC-MODEL, OPTIMIZATION, AGGREGATION, VARIABILITY, DEMAND, TIME
  • TED University Affiliated: Yes

Abstract

We propose a multistage stochastic mixed-integer programming formulation for the assignment of surgeries to operating rooms over a finite planning horizon. We consider the demand for and the duration of surgery to be random variables. The objective is to minimize three competing criteria: expected cost of surgery cancellations, patient waiting time, and operating room overtime. We discuss properties of the model and an implementation of the progressive hedging algorithm to find near-optimal surgery schedules. We conduct numerical experiments using data from a large hospital to identify managerial insights related to surgery planning and the avoidance of surgery cancellations. We compare the progressive hedging algorithm to an easy-to-implement heuristic for practical problem instances to estimate the value of the stochastic solution. Finally, we discuss an implementation of the progressive hedging algorithm within a rolling horizon framework for extended planning periods.