I work in the areas of decision analytics, operations and supply chain management, and public sector operations research. I have specific research interests in building forecasting and optimization (a.k.a. predictive and prescriptive analytics) decision models using data-driven optimization and machine learning.
My present focus is to address contemporary strategic and operational issues related to resource allocation and last-mile services delivery in healthcare, and urban logistics and mobility sectors.
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From predictive to prescriptive analytics: A data-driven multi-item newsvendor model Punia S., Singh S. P., Madaan J. By Decision Support Systems 136 - (2020)
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A cross-temporal hierarchical framework and deep learning for supply chain forecasting Punia S., Singh S. P., Madaan J. By Computers & Industrial Engineering 149 - (2021)
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Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail Punia S., Nikolopoulos K. , Singh S. P., Madaan J. , Litsiou K. By International Journal of Production Research 58 4964-4979 (2020)
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Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions Nikolopoulos K., Punia S. , Schäfers A. , Tsinopoulos C. , Vasilakis C. By European Journal of Operational Research 290 99-115 (2021)
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Predictive analytics for demand forecasting: A deep learning-based decision support system Punia S., Shankar S. By Knowledge-Based Systems 258 109956-109956 (2022)
Principal Investigator
- Effective, Efficient, and Equitable Allocations of Vaccines: A Strategic Decision Framework and a Multi-level Planning Model for National Vaccination Programs
- Ridership Forecasting in Public Transit Systems for Effective Operations Management
Ph. D. Students
Soumita Rakshit
Area of Research: Operations Management and Analytics
