Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation

TL;DR

This paper introduces an evolutionary algorithm that enhances patient admission scheduling by promoting solution diversity, leading to more robust and high-quality scheduling options in healthcare management.

Contribution

It is the first to adapt evolutionary diversity optimisation to a real-world combinatorial problem, specifically patient admission scheduling, with a novel diversity-biased mutation operator.

Findings

Diversity improves robustness of schedules

The proposed method outperforms traditional approaches

Simulation confirms the importance of diversity in scheduling

Abstract

Diversification in a set of solutions has become a hot research topic in the evolutionary computation community. It has been proven beneficial for optimisation problems in several ways, such as computing a diverse set of high-quality solutions and obtaining robustness against imperfect modeling. For the first time in the literature, we adapt the evolutionary diversity optimisation for a real-world combinatorial problem, namely patient admission scheduling. We introduce an evolutionary algorithm to achieve structural diversity in a set of solutions subjected to the quality of each solution. We also introduce a mutation operator biased towards diversity maximisation. Finally, we demonstrate the importance of diversity for the aforementioned problem through a simulation.