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Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach — arXiv:2608.30137

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30137
License (as archived): CC0-1.0
Archived: 2026-10-17T09:06:51.197Z
SHA-256 of the archived text: 05c082721e71585c48d0658928ae009c267574e74d2f588affd307f37a6cebd5
Stable URL of this page: https://metraipolis.com/documento/dc_fc7cb88c4e06

How to cite

Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach — arXiv:2608.30137 — arXiv (en), https://arxiv.org/abs/2608.30137 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc7cb88c4e06 (Metraipolis archive dc_fc7cb88c4e06, sha256 05c082721e71585c48d0658928ae009c267574e74d2f588affd307f37a6cebd5)

BibTeX

@misc{dcfc7cb88c4e06,
  title = {Enhancing Interpretability of Stochastic Programming Solutions: A Multiparametric Approach — arXiv:2608.30137},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30137},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc7cb88c4e06; sha256 05c082721e71585c48d0658928ae009c267574e74d2f588affd307f37a6cebd5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30137. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-09-01 in math.OC).
Categorie arXiv: Optimization and Control (math.OC) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.30137 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:40:19.758Z dalla pagina https://arxiv.org/catchup/math.OC/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).