{
  "abstract": "**Abstract (English)** Real-time vehicle routing and scheduling for massive-scale orders remains computationally prohibitive for exact solvers (like Gurobi/CPLEX) due to the exponential explosion of the state space. This paper presents a hybrid engine that integrates Adaptive Large Neighborhood Search (ALNS) with discrete topological state transitions (Swap, Reverse, Insert). We demonstrate its performance on an extreme-scale benchmark of 10,000 concurrent delivery orders. By applying a grid partition strategy for spatial dimensionality reduction, the pure C++ engine completed the allocation and routing in sub-second timeframe (128 milliseconds in single-core testing), achieving perfect compliance (zero time-window violations, zero capacity overloads). This baseline validation proves the viability of discrete topological optimization for ultra-fast commercial logistics SaaS.\n\n**摘要 (中文)** 面对海量订单的实时车辆路径与调度问题,由于状态空间的指数级爆炸,精确求解器(如 Gurobi/CPLEX)通常无法满足实时性要求。本文提出了一种融合自适应大邻域搜索(ALNS)与离散拓扑状态转换(交换、反转、插入)的混合引擎。我们在 10,000 个并发订单的极限基准测试上验证了其性能。通过引入网格分治策略进行空间降维,纯 C++ 编译的引擎在亚秒级时间内(单核实测 128 毫秒)完成了所有车辆的分配与路径规划,达成了完美合规(0 时间窗违规,0 容量超载)。该基准验证证明了离散拓扑优化在极速商业物流 SaaS 系统中的巨大可行性。",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Baoyuan Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7479cr6ap5c75.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23667428",
  "fulltext_url": null,
  "id": "7479cr6ap5c75",
  "issns": [],
  "landing_url": "https://zenodo.org/records/23667428",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://zenodo.org/api/records/23667428/files/paper_logistics_final.pdf/content",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7479cr6ap5c75.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:20:53Z",
      "native_id": "23667428",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23667428"
    }
  ],
  "subjects": [],
  "title": "Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search",
  "title_text": "Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search",
  "type": "preprint",
  "updated_at": "2026-11-09T12:20:53Z",
  "url": "https://research.draconitic.com/paper/7479cr6ap5c75",
  "venue": "Zenodo",
  "versions": []
}