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3446. Stochastic patient admission scheduling problem with an exponential number of scenarios

Invited abstract in session WD-10: Admission and discharge, stream OR in Health Services (ORAHS).

Wednesday, 14:30-16:00
Room: 11 (building: 116)

Authors (first author is the speaker)

1. Haichao Liu
School of Management, Northwestern polytechnical university
2. Jin-Kao Hao
LERIA, Université d’Angers
3. Yang Wang
School of Management, Northwestern Polytechnical University

Abstract

Hospital admission management is a fundamental challenge due to the uncertain demand for inpatient beds. This paper studies a stochastic variant of the well-known patient admission scheduling problem, which aims to assign patients to rooms during their hospitalizations while considering the overstay risk. We consider the problem as a two-stage stochastic programming problem where the first stage assigns patients to rooms on their planned hospitalization days, and the second stage evaluates the expected costs resulting from patient overstay. We propose two novel stochastic programming models to formulate the problem, including a scenario-based model and its equivalent state-variable model. The latter has a pseudo-polynomial number of variables and constraints, which is significantly smaller than the former. To solve the state-variable model efficiently, we apply the sample average approximation method to provide an initial feasible solution to the Gurobi solver. We carried out extensive computational experiments to evaluate the performance of the proposed models. The computational results revealed that our state-variable model finds high-quality feasible solutions with an average optimality gap of 3% for benchmark instances with 250 patients and 1.9E119 scenarios in 1 hour.

Keywords

Status: accepted

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