MapPoint can find the shortest route connecting a list of waypoints. Using the Optimize Stops function on the Route Planner, it can also find the best (‘optimum’) waypoint order that results in the shortest route. For example, you might need to visit three customers. The exact order is not important, but you want to do it with the shortest route possible in order to minimize costs. This is where MapPoint’s Optimize Stops function comes in.
Another name for this general function is the Travelling Salesman Problem – this finds the shortest route through a set of waypoints. The MapPoint implementation works with fixed start and end waypoints, and finds the optimum (or near optimum) order of the intermediate waypoints. The Travelling Salesman Problem is notoriously difficult for all but the most trivial problems. Therefore MapPoint uses a number of heuristics so that the majority of routes can be optimized in seconds or minutes.
Here is an example route that needs to be optimized:
It starts in Houston, ends in Shreveport, and includes three intermediate waypoints. The total distance is 711.2 miles with an estimated driving time of 12 hours 20 minutes (every 8 hours of driving time counts as one travel day due to an overnight stop).
Press the Optimize stops button to start the optimization process. Depending on the route distance and number of waypoints, this might take a few minutes. The following animated dialog box is displayed during the optimization process:
And here is the optimized route:
As you can see, the route is much more logical and does not include any backtracking. The new route has a distance of 605.8 miles with an estimated driving time of 10 hours 8 minutes. This new route is almost 15% shorter than the original.
MapPoint’s route optimization only uses route distances and estimated travel times. It cannot use other optimization factors such as time windows, and vehicle capacities. Delivery solutions which require these sophisticated features should investigate a professional delivery management system that supports these and other optimization factors.