Predictive and Prescriptive Analytics for Equitable and Efficient Service Provision in Smart Cities

预测性和规范性分析，以在智慧城市中提供公平和高效的服务

• 批准号: RGPIN-2022-04950
• 负责人: Liu, Sheng
• 金额: $ 1.97万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757684
• 关键词: Predictive; Prescriptive; Analytics; Equitable; Efficient

Providing services to residents in an equitable and efficient manner is a top priority for policymakers in Canada and around the world. In the private sector, online platforms empowered by gig workers have invented new ways of serving customers, harnessing the power of data analytics. However, without an adequate design of algorithms, a blind use of data may lead to suboptimal and biased outcomes. This proposal aims to develop data-driven mathematical models and algorithms built on predictive and prescriptive analytics tools to support service operations management, ensuring equity and efficiency for all citizens. Specifically, the proposal will focus on three closely related research topics that have not been well addressed in the literature: (1) data-driven facility location optimization with equity considerations; (2) crowd-sourcing resource allocation for on-time delivery of emergency supplies; and (3) joint capacity management and dispatching optimization under uncertainty. In the three projects, we will explore new models and methodologies that can drive decisions from data. Predictive and prescriptive analytics will be integrated carefully to enable theoretically optimal solutions with out-of-sample performance guarantees. To account for the spatiotemporal uncertainty of demand and supply in practical scenarios, we will also investigate robust optimization techniques in both single-period and multi-period settings. Because new problem structures can arise with the addition of equity constraints, we will design easily implementable and scalable algorithms to solve the resulting mathematical programs efficiently. The developed methods and algorithms will be tested on real-world problems in public services, healthcare, and last-mile logistics, with data collected from public sources and industry partners. Based on our extensive collaboration experience, we will seek implementation opportunities with different stakeholders (municipal governments, online platforms, and non-profit organizations). We expect the research outcome will bring theoretical breakthroughs and insightful policy suggestions to improve the life quality of Canadian residents.

以公平和高效的方式向居民提供服务是加拿大和世界各地政策制定者的首要任务。在私营部门，由零工工作人员支持的在线平台利用数据分析的力量，发明了新的服务客户的方式。然而，如果没有适当的算法设计，盲目使用数据可能会导致次优和有偏见的结果。该提案旨在开发基于预测性和规范性分析工具的数据驱动的数学模型和算法，以支持服务业务管理，确保所有公民的公平和效率。具体地说，该提案将集中于三个密切相关的研究主题，这些主题在文献中没有得到很好的解决：(1)考虑公平的数据驱动的设施选址优化；(2)为及时交付应急物资而进行的众包资源分配；(3)不确定情况下的联合能力管理和调度优化。在这三个项目中，我们将探索能够从数据中驱动决策的新模型和方法。预测性和规范性分析将仔细整合，以实现理论上的最优解决方案，并提供超出样本的性能保证。为了解决实际场景中需求和供给的时空不确定性，我们还将研究单周期和多周期设置下的稳健优化技术。由于新的问题结构可能会随着股权约束的增加而出现，因此我们将设计易于实现和可扩展的算法来高效地解决所产生的数学规划。开发的方法和算法将在公共服务、医疗保健和最后一英里物流的真实问题上进行测试，数据收集自公共来源和行业合作伙伴。基于我们广泛的协作经验，我们将寻求与不同利益相关者(市政府、在线平台和非营利组织)的实施机会。我们期待研究成果将为提高加拿大居民的生活质量带来理论突破和富有洞察力的政策建议。