Simulation optimisation for the housing of homeless populations.

无家可归者住房的模拟优化。

• 批准号: 2753394
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753394
• 关键词: Simulation; optimisation; housing; homeless; populations.

Government bodies around the world are responsible for 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 a queue. Due to the complex nature of such queueing systems, simulation modelling plays a vital role in estimating the societal impact of housing development plans. Simulation models are good at capturing the complexities of the real-world system but using them to find an optimal development plan is difficult. This project aims to use novel simulation optimisation algorithms to find near-optimal development plans in the context of homeless care systems. These algorithms will use a mathematical description of the queue dynamics alongside a simulation model to efficiently explore different development options. The simulation model will rely on forecasts for future homelessness which are highly uncertain. The challenge here is to appropriately consider the impact of this uncertainty on the simulation output and on the optimisation results. A further challenge is the fact that housing development plans are typically revisited - to account for this in the context of simulation optimisation will be a novel development.In partnership with the Naval Postgraduate School, Monterey, US.

世界各地的政府机构负责住房，确保无家可归者不会长期无人收容，并从长远来看能够获得高质量的住所。要做到这一点，他们必须在有限的预算内平衡紧急避难所的紧急需求和永久住房的长期需求。通过政府无家可归者护理系统的人流可以被建模为排队。由于这类排队系统的复杂性，模拟模型在估计住房开发计划的社会影响方面起着至关重要的作用。仿真模型擅长捕捉真实世界系统的复杂性，但使用它们来寻找最优的开发计划是困难的。该项目旨在使用新的模拟优化算法，在无家可归者护理系统的背景下找到接近最佳的发展计划。这些算法将使用队列动态的数学描述以及模拟模型来有效地探索不同的开发选项。模拟模型将依赖于对未来无家可归者的预测，这些预测具有高度的不确定性。这里的挑战是适当地考虑这种不确定性对模拟输出和优化结果的影响。另一个挑战是住房开发计划通常会被重新考虑--在模拟优化的背景下说明这一点将是一个新的发展。