Crate aprender_tsp 

aprender-tsp: Local TSP optimization with personalized .apr models.

This crate provides command-line tools for training, optimizing, and deploying Traveling Salesman Problem (TSP) solvers using local .apr model files.

Features

• Train personalized TSP models from your own problem instances
• Optimize routes using state-of-the-art metaheuristics (ACO, Tabu Search, GA)
• Export solutions in standard formats (JSON, CSV)
• Deploy offline with zero cloud dependency

Example

use aprender_tsp::{TspInstance, AcoSolver, TspSolver, Budget};

// Create a simple instance
let coords = vec![(0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0)];
let instance = TspInstance::from_coords("square", coords).unwrap();

// Solve with ACO
let mut solver = AcoSolver::new().with_seed(42);
let solution = solver.solve(&instance, Budget::Iterations(100)).unwrap();

println!("Tour length: {:.2}", solution.length);

Toyota Way Principles

• Genchi Genbutsu: Users understand their logistics problems best
• Kaizen: Continuous model improvement through incremental updates
• Jidoka: Build quality in through checksums and validation

Re-exports

pub use error::TspError;

pub use error::TspResult;

pub use instance::TspInstance;

pub use model::TspModel;

pub use solver::AcoSolver;

pub use solver::Budget;

pub use solver::GaSolver;

pub use solver::HybridSolver;

pub use solver::SolutionTier;

pub use solver::TabuSolver;

pub use solver::TspAlgorithm;

pub use solver::TspSolution;

pub use solver::TspSolver;

Modules

error

TSP-specific error types with actionable hints.

instance

TSP instance representation and parsers.

model

TSP model persistence in .apr format.

solver

TSP solver implementations.