Bilal Majeed
Ph.D. Candidate
Department of Industrial Engineering
University of Houston
Date: Friday, Jan 24, 2020
Time: 1 - 1:50 pm
Location: D3 W122
Abstract: Demand forecasting plays an important role in the deployment of mobile clinic services to target communities as it can help the service provider to maximize its coverage under limited resource. The challenge in the development of the forecasting model lies in the fact that the delinquency is only observed in schools for which very limited information is available, while rich demographic and economic information is available for census tracts but no observations of delinquency has been made at the census tract level. To address such a challenge, we first develop a hierarchical approach to forecast the demand of vaccinations in schools and census tracts. In the first stage of the hierarchical approach, we solve a linear optimization model to compute an association matrix that can align some common features in both the census tracts and school zones. Then we use the estimated association to develop a forecasting model to predict the vaccination delinquency in both schools and census tracts. A non-convex optimization model is also proposed to and the association matrix and the forecasting model simultaneously. A case study from Houston Independent School District (HISD) and its associated communities is reported to demonstrate the efficacy of the new model and technique.
Biography: Bilal Majeed is a third-year Ph.D. student in Industrial Engineering at University of Houston. His doctoral research is on vaccination demand forecasting via integrating multisource data. In his dissertation, he focuses on combining optimization methods and machine learning techniques to predict the accurate demand for vaccinations in target communities and develop an optimal deployment plan that can increase coverage. He also works as a research and teaching assistant at Department of Industrial Engineering, University of Houston.