Open Source Routing Engine for OpenStreetMap

Efficiently collect data from geographically distributed locations

AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

Open Source Routing Machine - C++ backend

This project demonstrates route optimization algorithms (Ant Colony Optimization and Nearest Neighbor) for urban logistics using real geographical coordinates and travel time data from the OpenRouteService API. It supports animated map visualizations and performance analysis.

Sit back and watch the bot water your crops using A* and TSP Greedy.

jsprit is a java based, open source toolkit for solving rich vehicle routing problems

Clustering traveling salesman solver for delivery and more.

Examples are written in Java dedicated to JOpt-TourOptimizer

The latest source code of our Angular-Demo Application.

High-performance weighted Voronoi stippling implementation. Exports PNG and TSP files. Visualizes TSP tours as continuous line drawings.

🚀✨ TSP Solver — Exact & heuristic algorithms 🧩📊 to tackle Traveling Salesman Problem 🚗💨! Includes DP, Backtracking, Branch & Bound, Christofides, Simulated Annealing 🔥🎯📈!

Efficient, scalable TSP solver for real-world coordinates using the Haversine formula. Instantly computes near-optimal routes for hundreds of geo-points.

A Streamlit app that finds the most time-efficient route between multiple addresses using the Google Maps API. Supports various transportation modes and generates a shareable Google Maps link with the optimized route.

Python REST client and examples for DNA-Evolutions TourOptimizer

A collection of algorithms and data structures

Optimize specific transports (single depot location, customer locations with pickups and deliveries) and displays the optimal routes for each of your vehicles on the map.

A collection of algorithms and data structures

TravelingSalesman: An alternative algorithm for the Traveling Salesman Problem using convex hull midpoints. This modified greedy approach offers a unique method for path optimization.