# Distributionally Robust Hospital Capacity Expansion Planning under Stochastic and Correlated Patient Demand

arXiv: https://arxiv.org/abs/2403.13234 · HTML: https://mlsanity.com/paper/2403.13234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Simulating Classification Models for Ex-Ante Evaluation of Predict-Then-Optimize Methods](https://mlsanity.com/paper/2509.02191/) (2025)

## Abstract (arXiv)

This paper investigates the optimal locations and capacities of hospital expansion facilities under uncertain future patient demands, considering both spatial and temporal correlations. We propose a novel two-stage distributionally robust optimization (DRO) model that integrates a Spatio-Temporal Neural Network (STNN). Specifically, we develop an STNN model that predicts future hospital occupancy levels considering spatial and temporal patterns in time-series datasets over a network of hospitals. The predictions of the STNN model are then used in the construction of the ambiguity set of the DRO model. To address computational challenges associated with two-stage DRO, we employ the linear-decision-rules technique to derive a tractable mixed-integer linear programming approximation. Extensive computational experiments conducted on real-world data demonstrate the superiority of the STNN model in minimizing forecast errors. Compared to neural network models built for each individual hospital, the proposed STNN model achieves a 53% improvement in average RMSE. Furthermore, the results demonstrate the value of incorporating spatiotemporal dependencies of demand uncertainty in the DRO model, as evidenced by out-of-sample analysis conducted with both ground truth data and under perfect information scenarios.