Using the Mapbox Optimization API is it possible to optimize the routes between multiple drivers?
Example: 6 locations are added, 2 drivers are added, the routes get split / optimized between the two drivers
I'm still in the planning stage, so I haven't poked around too much myself yet, but the code and all the examples I've seen are directed towards single driver optimization only... Has anybody done something like this before? Anything you can recommend to point me in the right direction?
Mapbox's Optimization API returns a duration-optimized route between the input coordinates, which is also known as solving the so-called "Travelling Salesman Problem". This is a well-known, NP-hard graph theory problem, meaning there is no general polynomial-time solution known for the problem.
The underlying data used for computing the aforementioned duration-optimized route are the cost functions of the edges connecting the coordinates input to the API request. You could retrieve the cost values (including traffic) between a set of these coordinate positions using Mapbox's Matrix API.
Adding a second driver/salesman to the problem makes the problem exponentially harder to solve, as discussed in the answer to this Stack Overflow post.
Here is a link to a scientific paper discussing a possible approach to this problem.
As evidenced by the research community, a solution for the Multiple Travelling Salesman Problem is not straightforward to implement. If you do not want to engage in this non-trivial task of implementing an algorithm that would solve it for you, you could implement a function that will make an educated guess on how to split up the destination coordinates between the two drivers. This "educated guess" could be based on values obtained from the Matrix API. You could make a one-to-many request for each driver, then take the lesser of the two durations for each coordinate and assign the coordinate to the appropriate driver. Then, you can use Mapbox's Optimization API to solve the two separate travelling salesman problems individually.
Even if you did implement an algorithm that would solve the Multiple Travelling Salesman Problem, the problem's complexity grows exponentially with the number of drivers and the number of waypoints. Therefore, you could end up with a solution that works, but would not necessarily compute in a reliable amount of time. These performance limitations are something to keep in mind when going about implementing a solution.