Academic TSP solutions try to generate an ideal one for this vital issue but the vast majority are not suitable as real-life decisions. The reason is that creating the best decision is time-consuming. In real life, time frequently is a crucial choice factor. For instance, a logistics company needs to figure out a route in minutes when planning its daily schedule because its results depend on both these shipping route planning decisions and avoiding drivers' downtime. So the business world requires not optimal TSP solutions but near-optimal ones in the shortest possible time, providing companies with the opportunity to plan routes quickly and efficiently.
There are different ways to solve the TSP problem other than the academic approaches. There are a lot of API solutions for optimization, but they are significantly limited by the number of waypoints to be optimized, with no time windows, no round trips, no capacity constraints, etc.
Applying the Distance Matrix API
, you can calculate the distance and the route time between each pair of locations taking into account real-time data. Keep in mind that depending on the particular task, you can make calculations taking into account the real-time traffic or not. Besides, the request calculation time is up to 50 elements per second, and you can get the API response in less than a second.
Distancematrix.ai API tool supports roads all over the world. Using it, you can assign the nearest driver based on proximity, provide job opportunities only for a particular drive time, and determine the nearest goods delivery point to a customer. Also, you can create a standard distance matrix or use a single origin with multiple destinations.
Distancematrix.ai also considerably facilitates the process when it is required to request a big matrix.