In the second part, we used what we learned to perform a VRP analysis. In this scenario, a trucking company in south Florida has one distribution center and we were to use VRP analysis to create optimal routes for one day's worth of pickups. Not only do we want the shortest route, we want to optimize the revenue and cost. In the first scenario, route zones for distribution center 4 were used exclusively. This means that each route zone had one and only one truck, and that truck could service only that zone, even if an order in another zone is closer. Since there are 14 zones, only 14 trucks are used. For a solution using these settings, there were a few orders that could not be completed and a few orders for which there were time violations.
After this, I created a new VRP using two more trucks. First, for routes in which the status was OK, which is where no orders were missed. For those routes, I set the attribute table to preserve those routes. Then I added two new trucks to the routes layer and solved the VRP. With the new solution, customer service was improved in a number of ways. First, all the orders were able to be completed with the new solution, as opposed to the 6 orders unable to be completed with the first solution. Additionally, there was only 1 time violation with the new route, as opposed to the first solution where more than one route had a time violation. The new solution also increases the revenue by $1625 while increasing the cost by $1152.43 as compared to the first solution, so the new solution slightly increases the profit. So the new solution improves customer service by being more efficient in that all the orders were serviced with fewer time violations. This is also beneficial to the company by increasing the company’s profits as compared to the first solution. Below is a screenshot of the routes after the addition of the two new routes.
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