# Logic-based Benders Decomposition and Binary Decision Diagram Based Approaches for Stochastic Distributed Operating Room Scheduling

arXiv: https://arxiv.org/abs/1907.13265 · HTML: https://mlsanity.com/paper/1907.13265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [Strong Optimal Classification Trees](https://mlsanity.com/paper/2103.15965/) (2021)
- [Predictive and prescriptive analytics for multi-site modelling of frail and elderly patient services](https://mlsanity.com/paper/2311.07283/) (2023)

## Abstract (arXiv)

The distributed operating room (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this problem where surgery durations are considered to be uncertain. In order to obtain solutions for the challenging stochastic model, we use sample average approximation, and develop two enhanced decomposition frameworks that use logic-based Benders (LBBD) optimality cuts and binary decision diagram based Benders cuts. Specifically, to the best of our knowledge, deriving LBBD optimality cuts in a stochastic programming context is new to the literature. Our computational experiments on a hospital dataset illustrate that the stochastic formulation generates robust schedules, and that our algorithms improve the computational efficiency.