PyVRP，一个专门解决车辆路径问题的Python开源库

PyVRP: A Python Library for Vehicle Routing Problems

PyVRP is a Python library designed to address the Capacity Constrained Vehicle Routing Problem (VRP). VRP is a complex combinatorial optimization challenge that involves planning vehicle routes in a way that minimizes total distance traveled, the number of vehicles used, and transportation costs, among other factors.

Installation and Basic Usage

The library can be easily installed using the package manager pip with the command: pip install pyvrp. An example code snippet is provided to demonstrate the basic usage of PyVRP in solving a VRP issue. The code includes defining locations and demands, adding vehicle types with various parameters such as number available, capacity, and cost, calculating a Manhattan distance matrix, and visualizing the client coordinates on a plot.

Key Functions and Visualization

An important encapsulated function within PyVRP is add_vehicle_type, which allows users to set vital VRP parameters such as the number of vehicles, their capacity, cost, and earliest/latest transportation times. The library also contains a plotting function to visualize the final routing solution, making it very convenient for users to see the results of their VRP solutions.

Search Parameter Customization and Solution Methods

While PyVRP comes with default search parameters to find optimal solutions, users have the flexibility to customize or modify these parameters. The library includes methods such as 2-opt and various exchange techniques. Overall, it stands out as a comprehensive and user-friendly library for solving VRP problems.

Underlying Algorithms

For those interested in the solution methods employed by PyVRP, the library utilizes a Hybrid Genetic Search (HGS) algorithm. This can be understood as a combination of genetic algorithms and neighborhood search. For a detailed understanding of the solution principles, users can refer to two scientific articles provided in the original content.

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