PhD project: Simulation optimisation for the housing of homeless populations
Government bodies around the world are responsible for the housing which ensures that homeless people are not left unsheltered for extended periods of time and have access to good quality accommodation in the long run. To do this, they must balance the urgent requirements for emergency shelter with the long-term need for permanent housing, all within a limited budget.
The flow of people through government homeless care systems can be modelled as as queue. Due to the complexities and instability of such queueing systems, simulation modelling plays a vital role in estimating the societal impact of housing development plans, which we call solutions. Simulation models are good at capturing the complexities of the real-world system but using them to find an optimal solution is difficult because we can only obtain samples from the true distribution of the output of interest, such as the amount of unmet need. This PhD project aims to use novel simulation optimisation (SO) algorithms to find near-optimal solutions in the context of homeless care systems. These algorithms will use the approach of meta-modelling. An analytical model of a simplified version of the homeless care system can be used as a meta-model to quickly evaluate a solution and efficiently guide the search of a SO algorithm.
The input models for the simulation will rely on forecasts for future homelessness which are highly uncertain. This uncertainty in the input models propagates through to the simulation output and is known as input uncertainty. The challenge in this research is to appropriately consider the impact of this input uncertainty on the simulation output and on the optimisation results. A further challenge is the fact that housing development plans are typically revisited. This adds a dynamic element to the decision-making processes which is rarely considered in the context of simulation optimisation.