{
  "abstract": "This paper studies the problem of controlling fluid approximations of processing networks under uncertainty. Classical robust formulations for such models rely on static (here-and-now) control decisions that must remain feasible for all realizations of uncertainty, often leading to highly conservative solutions. We propose a partially adaptive robust optimization framework that incorporates observed system information into the control policy at the planning point, enabling less conservative and more responsive decisions while retaining worst-case guarantees. Our approach begins by modeling the fluid network dynamics as a Separated Continuous Linear Program (SCLP) in the server-effort formulation. We introduce continuous-time extensions of the budgeted uncertainty set and develop three families of uncertainty structures appropriate for fluid-flow systems. For each family, we derive the corresponding robust counterpart. Since these robust counterparts include uncertainty in the objective, they cannot be solved directly by the classical SCLP--Simplex method. To address this challenge, we develop a hybrid algorithm that integrates an extended SCLP framework with the Saddle-Point Frank--Wolfe (SP--FW) method. This hybrid approach combines tractable continuous-time optimization with efficient saddle-point updates, yielding feasible and near-optimal partially adaptive robust solutions. The resulting methodology advances robust fluid control by enabling uncertainty-aware, dynamically informed decision policies while preserving computational tractability.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.25513",
  "author_total": 3,
  "authors": [
    {
      "name": "Roi Ben-Gigi"
    },
    {
      "name": "Evgeny Shindin"
    },
    {
      "name": "and Shimrit Shtern"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bgpzkhqw5dsyt.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.25513",
  "fulltext_url": null,
  "id": "bgpzkhqw5dsyt",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.25513",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "1803|Risk and Portfolio Optimization"
    ]
  ],
  "openalex": "W7222998524",
  "pdf_url": "https://arxiv.org/pdf/2610.25513",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bgpzkhqw5dsyt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:57Z",
      "native_id": "2610.25513",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.25513"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Partially Adaptive Robust Optimization for Uncertain Fluid Processing Networks",
  "title_text": "Partially Adaptive Robust Optimization for Uncertain Fluid Processing Networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:44Z",
  "url": "https://research.draconitic.com/paper/bgpzkhqw5dsyt",
  "venue": "arXiv",
  "versions": []
}