Surgical scheduling under uncertainty by approximate dynamic programming.
No Thumbnail Available
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Surgical scheduling consists of selecting surgeries to be performed within a day, while jointly assigning
operating rooms, starting times and the required resources. Patients can be elective or emergency/urgent.
The scheduling of surgeries in an operating theatre with common resources to emergency or urgent and
elective cases is highly subject to uncertainties not only on the duration of an intervention but mainly on
the arrival of emergency or urgent cases. At the beginning of the day we are given a candidate set of elective surgeries with and an expected duration and a time window the surgery must start, but the expected
duration and the time window of an emergency or urgent case become known when the surgery arrives.
The day is divided into decision stages. Due to the dynamic nature of the problem, at the beginning of
each stage the planner can make decisions taking into account the new information available. Decisions
can be to schedule arriving surgeries, and to reschedule or cancel surgeries not started yet. The objective
is to minimize the total expected cost composed of terms related to refusing arriving surgeries, to canceling scheduled surgeries, and to starting surgeries out of their time window. We address the problem
with an approximate dynamic programming approach embedding an integer programming formulation
to support decision making. We propose a dynamic model and an approximate policy iteration algorithm
making use of basis functions to capture the impact of decisions to the future stages. Computational experiments have shown with statistical significance that the proposed algorithm outperforms a lookahead
reoptimization approach.
Description
Keywords
Approximate policy iteration algorithm
Citation
SILVA, T. A. de O.; SOUZA, M. C. de. Surgical scheduling under uncertainty by approximate dynamic programming. Omega, v. 95, artigo 102066, 2020. Disponível em: <https://www.sciencedirect.com/science/article/abs/pii/S0305048318309381>. Acesso em: 12 set. 2021.