Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach
Abstract
Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural properties of the recourse problem that drive optimal first-stage decisions. This work introduces a novel, deterministic approach to explainability using multiparametric programming (mp) within a Benders decomposition framework. We reformulate the recourse subproblem as a multiparametric linear program, generating an explicit map of Critical Regions (CRs), which are polyhedral partitions of the uncertainty space. This allows us to cluster scenarios analytically rather than statistically. We demonstrate this methodology on a supply chain planning under demand uncertainty. Our results show that 100 stochastic scenarios map to exactly six critical region clusters. This mapping allows us to explain optimal capacity planning decisions as a precise trade-off between specific operational modes, providing a fully transparent interpretation of the stochastic solution.
Versions
Related
- Dynamic programming principle for controlled systems driven by fractional Brownian motions and related backward stochastic Riccati equations
- Scentree: a framework for generating scenario trees for multistage stochastic programming
- Fused ultrametric Gromov-Wasserstein for Scenario Tree generation in Multistage Stochastic Programming
- Adaptive Variance-Reduced Stochastic Sequential (Quadratic Constrained) Quadratic Programming
- Computing Approximate Solutions of Inconsistent Max-Min Fuzzy Relational Equations by Mixed-Integer Programming
BibTeX
@misc{brahmbhatt2026enhancing,
title = {{Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach}},
author = {Parth Brahmbhatt and Styliani Avraamidou},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.30137},
eprint = {2608.30137},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.30137},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach
AU - Parth Brahmbhatt
AU - Styliani Avraamidou
PY - 2026
DA - 2026/08/31
PB - arXiv
DO - 10.48550/arxiv.2608.30137
UR - https://arxiv.org/abs/2608.30137
AB - Stochastic programming (SP) is a powerful framework for decision-making under uncertainty, but its practical adoption in industry is often hindered by the difficulty in understanding the causal relationships that drive optimal solutions. In the two-stage SP, strategic first-stage decisions are coupled with operational second-stage recourse decisions. When the number of scenarios under consideration is large, understanding the direct link between the uncertainty realization and optimal recourse strategy becomes computationally and cognitively demanding. Common approaches to improve interpretability include trained classification trees or scenario reduction, replacing the large scenario set with a representative subset. This is often achieved through post-hoc clustering (e.g., k-means) based on uncertainty realizations or optimal recourse decisions. While useful, these methods only provide a statistical approximation of the solution space and may fail to reveal the underlying structural properties of the recourse problem that drive optimal first-stage decisions. This work introduces a novel, deterministic approach to explainability using multiparametric programming (mp) within a Benders decomposition framework. We reformulate the recourse subproblem as a multiparametric linear program, generating an explicit map of Critical Regions (CRs), which are polyhedral partitions of the uncertainty space. This allows us to cluster scenarios analytically rather than statistically. We demonstrate this methodology on a supply chain planning under demand uncertainty. Our results show that 100 stochastic scenarios map to exactly six critical region clusters. This mapping allows us to explain optimal capacity planning decisions as a precise trade-off between specific operational modes, providing a fully transparent interpretation of the stochastic solution.
ER -