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In this talk, we address a high-fidelity attended home healthcare scheduling and routing problem with unpredictable newcomer arrivals. We develop a novel adaptive optimization framework that integrates risk-aware uncertainty sets, enabling real-time adjustments to on-demand newcomers while preserving computational tractability. Our model explicitly balances route consistency for long-term patients with dynamic newcomer integration, achieving superior performance. We also develop a tailored branch-price-and-cut algorithm to efficiently solve instances. Computational experiments demonstrate that our framework resolves systemic inefficiencies in workforce utilization and service reliability endemic to conventional AHH policies, while without sacrificing out-of-sample operating costs. We empirically identify the under-performance of baseline policies as stemming from their inability to accommodate on-demand newcomers, which necessitates a granular characterization of uncertainties and real-time adaptability with proactive scenario scheduling.

(This is a joint work with Mingda Liu from BJTU and Xiaolei Xie from Tsinghua University)

A short Bio: Chun Peng, Associate Professor in Department of Logistics Management in Beijing Jiaotong University (BJTU). Before joining BJTU in 2022, he was working in GERAD and Department of Decision Sciences in HEC Montréal as a postdoctoral researcher. His research mainly focuses on data-driven decision-making under uncertainty with applications to logistics transportation and supply chain management. He has published his research in renowned scientific journals, e.g., Operations Research, Transportation Science, EJOR, Omega. His research is partially supported by National Natural Science Foundation of China (NSFC) and industrial partners.