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

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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.