These are the three basic steps to solve a real world vehicle routing problem with GraphHopper’s Optimization API: 1) Assign geo locations to (road) network 2) Calculate travel times and distances 3) Solve vehicle routing problem Each step impacts the solution of the vehicle routing problem. This article illustrates how 1) – the assignment of […]

This article describes how we calculate and prepare travel time matrices to solve time dependent vehicle routing problems. Let me first explain what a typical vehicle routing problem is: Given a vehicle fleet and a number of customers, find the optimal set of vehicle routes to serve all customers and to minimize total transportation costs, […]

Load balancing in the context of vehicle routing problems is a way to achieve a more evenly distribution of jobs or activities over your drivers’ routes. There are a number of reasons to balance load. The most obvious ones are resource utilization and fairness. Why do we need a blog post for this? Why aren’t […]

We are happy to announce our new product: time-dependent route optimization. To illustrate what this actually means, let us assume we have a number of drivers who need to serve 40 customers in London within a time window of 2 hours. All drivers start from the airport London Heathrow and do not need to return […]

Introduction In this tutorial we are going to show you how to model a vehicle routing problem with vehicles that can have multiple return trips to the depot or hub.For example, let us assume you have 2 drivers that have a car much like the one on the figure below. It has an electric motor […]

Every optimization requires an objective ie. a goal to which the results should be optimize for. Here, we want to illustrate the differences between two objectives: min transport time and min completion time. Using the GraphHopper Directions API the specification of these goals is as simple and intuitive as the following json snippets: or Transport […]