Scalable Finite Adaptability via Polyhedral Partition and Learning
We study finite adaptability for decision-making under uncertainty, where a small set of candidate solutions is prepared in advance and the best response is selected after uncertainty is realized. While existing methods have made significant progress on exact formulations, scalability remains a persistent challenge due to (i) the combinatorial nature of assigning decisions to uncertainty realizations, and (ii) the joint optimization of uncertainty set partition and subsequent decisions. We propose a framework that makes the partition of the uncertainty set explicit and uses polyhedral partitions as the basis for policy design. Under mild regularity conditions and for general risk measures, we show that such policies converge to the optimal fully adjustable policy as the number of regions increases. Building on this result, we develop a parametric partition framework that allows flexible policy design with tractable reformulations for both robust and stochastic finite adaptability problems. To improve scalability, we introduce an approximate-learn-parallel framework that integrates partition learning with parallel optimization while preserving solution robustness. Computational experiments on classical testbeds in both robust and stochastic settings show that the proposed method scales to larger instances and yields competitive policy performance.