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Data Tech 2026 has ended

Friday May 15, 2026 11:45am - 12:15pm CDT
Effective inventory management across multiple regions is often hindered by stochastic demand, inter-regional dependencies, and lead-time uncertainties. In this study, we introduce a spatially-aware reinforcement learning framework (SAR-Q-Learning) that integrates region-specific demand forecasting with multi-agent Q-learning to optimize inventory decisions across a network of warehouses. Using the UCI Online Retail dataset, we estimate autoregressive demand dynamics for each region and incorporate spatial correlations to guide policy learning. Empirical results show that the SARQ-Learning agent successfully balances service levels, holding costs, and stockouts, achieving higher mean rewards and more stable final inventories compared to naive or independent policies. Visual analyses of weekly sales, AR(1) parameters, training convergence, and forecasted inventory trajectories demonstrate the model’s capability to capture complex temporal and spatial patterns. These findings underscore the potential of spatially-informed reinforcement learning for robust, data-driven inventory control in multi-regional supply networks
Speakers
avatar for Jong-Min Kim, PhD

Jong-Min Kim, PhD

Full Professor, University of Minnesota at Morris
Dr. Jong-Min Kim is a Professor of Statistics at the University of Minnesota–Morris, USA. Since June 2025, he has also served as an Adjunct Professor at the EGADE Business School, Tecnológico de Monterrey, Mexico.
Friday May 15, 2026 11:45am - 12:15pm CDT
(g) Minnetonka Best Buy HQ, 7700 Knox Ave S, Richfield, MN 55423

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