{
  "abstract": "Optimal scheduling of combined chemo-immunotherapy is often formulated as a control-affine optimal control problem, which generically yields boundary-selected (bang-bang-type) protocols unless singular arcs occur. This structure, while convenient, neglects saturating pharmacodynamics at high dose rates. We incorporate Michaelis-Menten saturation directly into the therapy channels, making the dynamics non-control-affine and the Hamiltonian nonlinear in each control. With strictly positive exposure penalties, the Hamiltonian admits an explicit three-regime pointwise minimization law: each input is chosen at the lower bound, the upper bound, or as a unique interior minimizer. On interior intervals the strict Legendre condition holds, so continuously modulated dosing arises as a regular interior extremal rather than a singular-arc or smoothing artifact. The resulting transcription is nonconvex; we therefore use a hybrid Quantum Particle Swarm Optimization (QPSO)-Sequential Quadratic Programming (SQP) pipeline, where QPSO provides a constraint-aware warm start and SQP enforces feasibility and Karush-Kuhn-Tucker (KKT) optimality on a collocation grid. Costate reconstruction corroborates the Pontryagin Minimum Principle (PMP) structure and the predicted boundary/interior regime transitions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Discrete and Continuous Dynamical Systems - Series B, 38 (2026), 280-302",
  "arxiv": "2609.11213",
  "author_total": 3,
  "authors": [
    {
      "name": "Bereket Sitotaw Kidane"
    },
    {
      "name": "Md Samiul Haque Motayed"
    },
    {
      "name": "Shuo Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bte298se3ah0v.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.11213",
  "fulltext_url": null,
  "id": "bte298se3ah0v",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.11213",
  "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.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.11213",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bte298se3ah0v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:30Z",
      "native_id": "2609.11213",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.11213"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Optimal chemo-immunotherapy scheduling: A hybrid QPSO-SQP approach with Michaelis-Menten pharmacodynamics",
  "title_text": "Optimal chemo-immunotherapy scheduling: A hybrid QPSO-SQP approach with Michaelis-Menten pharmacodynamics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:30Z",
  "url": "https://research.draconitic.com/paper/bte298se3ah0v",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.3934/dcdsb.2026072",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}