In this example, we’ll solve a simple facility location problem: where to build warehouses to supply a large number of supermarkets.
We’ll construct a mathematical model of the business problem, implement this model in the Gurobi Python interface, and compute and visualize an optimal solution.
Although your own business may not involve supermarkets, the same basic techniques used in this example can be used for many other applications in supply chain, logistics, and transportation.
A large supermarket chain in the UK needs to build warehouses for a set of supermarkets it is opening in Northern England. The locations of the supermarkets have been decided, but the locations of the warehouses are yet to be chosen.
Several good candidate locations for the warehouses have been determined, but it remains to decide how many warehouses to open and at which candidate locations to build them.
Opening many warehouses would be advantageous as this would reduce the average distance a truck has to drive from warehouse to supermarket and hence reduce the delivery cost. However, opening a warehouse is costly.
We will use Gurobi to find the optimal tradeoff between delivery cost and the cost of building new facilities. https://demos.gurobi.com/facility-location
To access the facility location demo application and create your scenario using your own data from a blank template or to play with existing default scenarios, you must first register for a Gurobi website account and then view the demo.
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