Lab 5 - Vehicle Routing Problem

Screen capture of a solved vehicle routing problem.

This week's lab focused on the vehicle routing problem, and how ArcGIS Network Analyst extension solves for this problem.  The screenshot above shows the results of the analysis, which is a series of 22 routes with various delivery stops.

Solving for any vehicle routing problem (VRP) can become a bit complex as behind-the-scenes work just to build up the truck and employee cost information, customer information, and depot information can be very extensive.  This does not even begin to include the use of a suitable road network on which to model the routes, or the definitions of where each truck/route can go... happily the great majority of this work was completed for us prior to beginning this lab.

The solved routes in the screenshot above utilize all 22 trucks in a distribution company's fleet.  Each truck was assigned to a certain "route zone" meaning that they ideally do not leave their zones to make deliveries in other areas.  However a few trucks were allowed to stray outside of their routine service zones in order to be part of a more profitable solution for this particular company.  Why have a service zone?  In order to provide continuity for the customer by having the same delivery man, instead of random unknowns doing the drop-offs. 

The one big issue with making service zones overly strict is this: the VRP solver can miss the obvious 'common-sense' solution in favor of an optimal solution that adheres to strict parameters.  Prior to the screenshot above our initial VRP route had been solved with such strictly defined service zones.  As operating costs are also an important factor, the VRP solver had provided only 14 routes (meaning 14 trucks) to shoulder the burden of delivering 128 orders across southern Florida.  This meant a lot of overtime, and in the end there were 6 orders left unfulfilled and several others that would have been delivered outside of regular business hours.

By tweaking a few items (mainly allowing a few trucks to make deliveries in adjacent delivery zones) the VRP solver was able to assign a truck route to deliver all of the orders, with only 1 order (out of 128) being made after normal business hours.  The overall revenue generated with the modified route also went up ... along the with cost to operate, but the operating costs were comparable to the rise in revenue.  Assuming that a satisfied customer is one that does repeat business, then choosing the optimal route solely on the lowest operating cost simply does not make sense - one needs to also make sure that the customer's needs are being met.  This was accomplished with the modified VRP route.