{
  "abstract": "Multiproduct pipelines transport refined products to delivery stations in sequential batches, and their detailed scheduling must meet market demand while respecting nonlinear hydraulic constraints. Retaining the full hydraulic nonlinearity yields a mixed-integer nonlinear program that is computationally intractable, while the linearization commonly adopted for solver-based optimization degrades hydraulic accuracy without resolving the heavy computational burden. This paper develops an action-decoupled proximal policy optimization (PPO) framework that efficiently produces detailed schedules while preserving hydraulic fidelity. The scheduling process is cast as a semi-Markov decision process (SMDP) with flexible action durations. To handle the discrete-continuous hybrid action space, a cascaded dual-actor architecture decouples the discrete operating duration from the continuous flow decisions, eliminating the gradient interference that destabilizes conventional coupled-action policies and thereby yielding high-quality schedules far more efficiently. Validated on a real-world multiproduct pipeline in China and benchmarked against a single-actor baseline and a linearized MILP solved by Gurobi, the proposed framework achieves delivery accuracy close to the exact optimum while substantially improving flow rate stability and substantially reducing solution time.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.02823",
  "author_total": 5,
  "authors": [
    {
      "name": "Kai Shao"
    },
    {
      "name": "Ling Jian"
    },
    {
      "name": "Liming Liu"
    },
    {
      "name": "Serguey V. Bogomolov"
    },
    {
      "name": "Xinmin Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f2j2s75wd99za.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.02823",
  "fulltext_url": null,
  "id": "f2j2s75wd99za",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.02823",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.02823",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f2j2s75wd99za.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:10Z",
      "native_id": "2611.02823",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.02823"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Efficient Detailed Scheduling of Multiproduct Pipelines using Action-Decoupled Deep Reinforcement Learning",
  "title_text": "Efficient Detailed Scheduling of Multiproduct Pipelines using Action-Decoupled Deep Reinforcement Learning",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:22Z",
  "url": "https://research.draconitic.com/paper/f2j2s75wd99za",
  "venue": "arXiv",
  "versions": []
}