Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search
- 1Independent Researcher
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.
**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。
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BibTeX
@misc{zhang2026extremescale,
title = {{Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search}},
author = {Baoyuan Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23667428},
url = {https://zenodo.org/records/23667428},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search
AU - Baoyuan Zhang
PY - 2026
DA - 2026/10/26
PB - Zenodo
DO - 10.5281/zenodo.23667428
UR - https://zenodo.org/records/23667428
AB - **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. **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。
ER -