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Attend this research seminar to explore an innovative optimization framework for attended home healthcare scheduling that addresses the challenge of unpredictable newcomer arrivals. Discover how risk-aware uncertainty sets enable real-time adjustments to accommodate on-demand patients while maintaining computational efficiency. Learn about the novel adaptive optimization approach that balances route consistency for long-term patients with dynamic newcomer integration, achieving superior performance compared to conventional policies. Examine the tailored branch-price-and-cut algorithm developed to efficiently solve complex scheduling instances. Understand how this framework resolves systemic inefficiencies in workforce utilization and service reliability that plague traditional attended home healthcare policies, without compromising out-of-sample operating costs. Gain insights into why baseline policies underperform due to their inability to accommodate on-demand newcomers and how granular uncertainty characterization combined with real-time adaptability and proactive scenario scheduling provides the solution. This joint research presentation by Chun Peng from Beijing Jiaotong University, conducted in collaboration with Mingda Liu from BJTU and Xiaolei Xie from Tsinghua University, offers valuable perspectives on advancing healthcare service delivery through robust optimization strategies.
