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

Cheng Guo (cguo***at***mie.utoronto.ca)
Merve Bodur (bodur***at***mie.utoronto.ca)
Dionne M. Aleman (aleman***at***mie.utoronto.ca)
David R. Urbach (david.urbach***at***wchospital.ca)

Abstract: 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.

Keywords: distributed operating room scheduling; stochastic integer programming; logic-based Benders decomposition; binary decision diagram

Category 1: Stochastic Programming

Category 2: Integer Programming (Cutting Plane Approaches )

Category 3: Applications -- OR and Management Sciences (Scheduling )


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Entry Submitted: 07/30/2019
Entry Accepted: 07/31/2019
Entry Last Modified: 06/10/2020

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