The VRP solver for large-scale, real-world routing optimization

List decision variables are a very powerful modeling feature for various problems where collections of items have to be ordered in an optimized fashion. Indeed, the value of a list decision is a permutation, which makes these variables an ideal tool for representing the order of customers visited by the trucks in a vehicle routing problem. Thanks to Hexaly’s large collection of generic operators, list-based models for such problems are very compact and straightforward. Not only are they easier to write than classic MILP models, they also allow Hexaly to scale well and achieve state-of-the-art results even on very large instances.

Thanks to Hexaly’s comprehensive set of generic mathematical operators, you can virtually write any constraint or objective you might think of. The easiest way to write a custom vehicle routing model is to start from one of our many routing code templates, and change the parts that need customizing.

Thanks to Hexaly’s list-based modeling formalism, vehicle routing models written with Hexaly are very compact and straightforward. Not only are they easier to write than classic MILP models, they also allow Hexaly to scale well and achieve state-of-the-art results even on very large instances. For more information, check out our routing benchmarks page, comparing Hexaly with the best solvers in the competition.

The no-code modeler, available within Hexaly Studio, is a powerful tool for building a model with Hexaly Modeler according to the parameters of your problem. It is able to combine dozens of specifications in order to perfectly match your needs. You can use it to model your routing problem with the configuration of your choice, with features such as Customer Time-Windows, Pick-up and Delivery Pairs, Rules & Regulations, Inventory Routing and much more, without writing a single line of code.

In addition to Hexaly Modeler, Hexaly’s own modeling and programming language, Hexaly can be used via Python, C++, C#, and Java APIs.

Deploying Hexaly in Docker is one of the most efficient ways to standardize your optimization environment, streamline automation, and ensure reproducibility across teams. This guide will walk you through the installation of Hexaly on a Linux-based Docker container using the official .run installer.

The easiest way to start modeling routing problems with Hexaly is to follow our vehicle routing tutorial. For more generic advice, you can check out our documentation on how to migrate from MIP to Hexaly.

With your Hexaly license (trial, business, or academic), you benefit from dedicated and responsive support from the experts who developed Hexaly and solved real-world problems for 25 years. Don’t hesitate to contact us if you need any help solving your routing optimization problems with Hexaly; we will be happy to assist you!