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 […]

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 […]

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 […]

Introduction In this tutorial we are going to show you how to model a vehicle routing problem where tasks do not only have multiple dependencies, but also require special skills. For example, let us assume we have two technicians called Peter and Stefan. Peter cannot only read the warm water meter, but he can also […]

Introduction In this tutorial we are going to show you how to model a traveling salesman problem with a week-planning horizon. For example, let us assume you have ONE worker that needs to visit 25 customers in the course of the next week. Let us also assume that the worker has specific daily working hours […]