{
  "abstract": "The growth of e-commerce has intensified the need for last-mile delivery systems that can jointly manage customer choice and operational efficiency. We study the Dynamic Offering and Pricing of Mixed Delivery Options problem, in which a logistics service provider dynamically selects and prices attended home-delivery time slots and out-of-home pickup options for sequentially arriving customers. Each decision affects immediate revenue, customer acceptance, and the route-dependent fulfillment cost realized at the end of the booking horizon. We formulate DOPMDO as a finite-horizon Markov decision process and propose State-Value Anchored Pricing via Approximate Dynamic Programming. The method learns a continuation-value approximation on an aggregate state representation of the mixed-delivery system and uses accepted-versus-rejected value differences to estimate option-level opportunity costs. These opportunity costs are embedded in an anchored trust-region pricing problem around a calibrated fine-static benchmark. Computational experiments on the real-world Seattle instance show that SVAP--ADP increases mean episode profit by 7.0\\% relative to current practice (95\\% CI: 6.7--7.2\\%), primarily by reducing terminal fulfillment cost while maintaining a stable home--locker--opt-out mix. Assortment-control experiments show that dynamic pricing is most effective when the menu exposes operationally valuable locker alternatives, with richer candidate pools delivering substantially larger gains than restricted nearest-locker menus. These results indicate that anticipatory pricing and assortment control are complementary: pricing steers customers toward lower-cost options, but the menu determines whether high-value consolidation opportunities are available in the first place.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13539",
  "author_total": 2,
  "authors": [
    {
      "name": "Ze Zhou"
    },
    {
      "name": "Balázs Kulcsár"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/enng6dqbqkeq8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13539",
  "fulltext_url": null,
  "id": "enng6dqbqkeq8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13539",
  "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",
      "2215|Urban and Freight Transport Logistics"
    ]
  ],
  "openalex": "W7213238948",
  "pdf_url": "https://arxiv.org/pdf/2609.13539",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/enng6dqbqkeq8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:29Z",
      "native_id": "2609.13539",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13539"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Assortment Control Unlocks the Value of Dynamic Pricing in Mixed Last-Mile Delivery",
  "title_text": "Assortment Control Unlocks the Value of Dynamic Pricing in Mixed Last-Mile Delivery",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:29Z",
  "url": "https://research.draconitic.com/paper/enng6dqbqkeq8",
  "venue": "arXiv",
  "versions": []
}