Extreme-Scale Real-Time Logistics Scheduling via Discrete Topological Search and Adaptive Large Neighborhood Search

**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。