Customer Assignment Problem
Many companies in various industries must, at some point, make strategic decisions about where to build facilities to support their operations. For example:
- Producers of goods need to decide how to design their supply chains which encompass factories, distribution centers, warehouses, and retail stores.
- Healthcare providers need to determine where to build hospitals to maximize their population coverage.
These are strategic decisions that are difficult to implement and costly to change because they entail long-term investments. Furthermore, these decisions have a significant impact, both in terms of customer satisfaction and cost management. One of the critical factors to consider in this process is the location of the customers that the company is planning to serve.
This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.
A mathematical model that selects the optimal placement of facilities (from a set of candidate locations) in order to minimize the distance between the company’s facilities and the customers.
Key Features of the Solution
- Application of the k-means algorithm to pre-process the customer location data.
- Deployment of a Binary Integer Program.
It is shown how machine learning can be leveraged to cope with big datasets
Request a Gurobi Evaluation License or Free Academic License
Modeling examples are coded using the Gurobi Python API in Jupyter Notebook. In order to use the Jupyter Notebooks, you must have a Gurobi License. If you do not have a license, you can request an Evaluation License as a Commercial User or download a free license as an Academic User.
Access the Jupyter Notebook Modeling Example
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