Technician Routing and Scheduling Problem

In this Technician Routing and Scheduling Problem (TRSP), you will formulate a multi-depot vehicle routing problem with the Gurobi Python API

Try this modeling example to discover how mathematical optimization can help telecommunications firms automate and improve their technician assignment, scheduling, and routing decisions in order to ensure the highest levels of customer satisfaction.

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 have some knowledge about building mathematical optimization models. To fully understand the content of this notebook, you should be familiar with object-oriented-programming.



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.


Commercial Users: Free Evaluation Version Academic Users: Free Academic Version



Access the Jupyter Notebook Modeling Example

Click on the button below to be directed to GitHub where you can download the repository for the Technician Routing and Scheduling Problem Coverage Jupyter Notebook modeling example.


Technician Routing and Scheduling Problem


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