A Robust Site Selection Model under uncertainty for Special Hospital Wards in Hong Kong

TL;DR

This paper develops robust site selection models for a Hong Kong hospital, incorporating uncertainty parameters and investigating symmetric and bounded uncertainties to improve scheduling under data variability.

Contribution

It introduces a new robust optimization framework for hospital site selection that accounts for multiple uncertainty parameters and applies a min-max approach for reliable solutions.

Findings

Models effectively handle uncertainty in site selection.

Proposed approach yields solutions resilient to data variability.

Framework applicable to MILP problems with uncertain data.

Abstract

This paper process two robust models for site selection problems for one of the major Hospitals in Hong Kong. Three parameters, namely, level of uncertainty, infeasibility tolerance as well as the level of reliability, are incorporated. Then, 2 kinds of uncertainty; that is, the symmetric and bounded uncertainties have been investigated. Therefore, the issue of scheduling under uncertainty has been considered wherein unknown problem factors could be illustrated via a given probability distribution function. In this regard, Lin, Janak, and Floudas (2004) introduced one of the newly developed strong optimisation protocols. Hence, computers as well as the chemical engineering [1069-1085] has been developed for considering uncertainty illustrated through a given probability distribution. Finally, our accurate optimisation protocol has been on the basis of a min-max framework and in a case of application to the (MILP) problems it produced a precise solution that has immunity to uncertain data.