物流路径优化 | Logistics Route Optimization
Solves logistics distribution route optimization with two modes — TSP (traveling salesman) and VRP (capacitated vehicle routing) — and outputs the optimal routes and total cost.
TSP: nearest-neighbor heuristic + 2-opt local search to find the shortest tour that visits all nodes and returns to the start. VRP: a greedy "demand descending + nearest feasible vehicle" rule assigns customers to vehicles (capacity constraint), and each vehicle's route is then internally optimized with TSP; a time-window feasibility check is also provided. Distances support both Haversine (lon/lat) and Euclidean metrics.
Dependencies / 依赖
pip install 'numpy' 'scipy' 'geopandas' 'shapely'
Usage / 使用方法
Basic usage
python geoskill-logistics-optimization.py --bbox 116.0 39.0 117.0 40.0 [other parameters]
Example 1 (TSP on synthetic nodes, offline)
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode tsp --output-dir ./out
Example 2 (VRP on synthetic nodes)
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode vrp --capacity 12 --output-dir ./out
Example 3 (real nodes GeoJSON, first point is the depot)
python geoskill-logistics-optimization.py --input nodes.geojson --mode vrp --capacity 15 --output-dir ./out
Example 4 (Euclidean distance + 2-opt disabled (faster))
python geoskill-logistics-optimization.py --input nodes.geojson --mode tsp --metric euclidean --no-2opt --output-dir ./out
Example 5 (more synthetic customers)
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode vrp --n-customers 30 --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
routes.geojson |
GeoJSON | Per-vehicle routes (LineString, with load/mileage) |
nodes.geojson |
GeoJSON | Depot and customer nodes (with demands) |
solution.json |
JSON | Optimal solution (vehicle count/total mileage/per-route detail) |
output-manifest.json |
JSON | Run manifest |
Data Source / 数据源 / Source
GeoJSON point features; the first point is the depot and the rest are customers; demand can be specified via the demand attribute. Alternatively, use --synthetic to generate physically consistent simulated data (fully offline).
Privacy / 隐私声明 / Privacy
- Offline by default;
--syntheticmode requires no network at all. - All processing is done locally; no user data is uploaded.
License / License
MIT
name: geoskill-logistics-optimization description: 'VRP/TSP with time windows and capacity constraints to compute optimal routes and cost for logistics'
物流路径优化 | Logistics Route Optimization
求解物流配送路径优化，支持 TSP（旅行商）与 VRP（带容量车辆路径）两种模式，输出最优路径与总成本。
TSP：最近邻启发式 + 2-opt 局部搜索求访问所有节点并返回起点的最短回路。VRP：按“需求降序 + 最近可装车”贪心把客户分配到车辆（容量约束），每辆车内部再做 TSP 优化；另提供时间窗可行性检查。距离支持 Haversine（经纬度）与欧氏两种度量。
依赖
pip install 'numpy' 'scipy' 'geopandas' 'shapely'
使用方法
基本用法
python geoskill-logistics-optimization.py --bbox 116.0 39.0 117.0 40.0 [其他参数]
示例 1（合成节点跑 TSP，离线）
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode tsp --output-dir ./out
示例 2（合成节点跑 VRP）
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode vrp --capacity 12 --output-dir ./out
示例 3（真实节点 GeoJSON（首点为仓库））
python geoskill-logistics-optimization.py --input nodes.geojson --mode vrp --capacity 15 --output-dir ./out
示例 4（欧氏距离 + 关闭 2-opt（更快））
python geoskill-logistics-optimization.py --input nodes.geojson --mode tsp --metric euclidean --no-2opt --output-dir ./out
示例 5（更多合成客户）
python geoskill-logistics-optimization.py --bbox 116 39 117 40 --synthetic --mode vrp --n-customers 30 --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
routes.geojson |
GeoJSON | 各车辆路径（LineString，含载重/里程） |
nodes.geojson |
GeoJSON | 仓库与客户节点（含需求） |
solution.json |
JSON | 优化解（车辆数/总里程/各路线） |
output-manifest.json |
JSON | 运行清单 |
数据源 / Source
GeoJSON 点要素，第一个点为仓库 depot，其余为客户；可用属性 demand 指定需求。 或使用 --synthetic 生成物理一致的模拟数据（完全离线）。
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传用户数据。
License
MIT