Traveling Salesman Problem

Traveling Salesman Problem finds the shortest possible route that visits each city once and returns to the original city.

In this example, you’ll learn how to tackle one of the most famous combinatorial optimization problems in existence: the Traveling Salesman Problem (TSP). The goal of the TSP – to find the shortest possible route that visits each city once and returns to the original city – is simple, but solving the problem is a complex and challenging endeavor. We’ll show you how to do it!

This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

 


 

Access the Jupyter Notebook Modeling Example

Click on the link below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.

 

Traveling Salesman Problem

 

How to Run the Jupyter Notebook Modeling Example

-To run the example the first time, choose “Runtime” and then click “Run all”.

-All the cells in the Jupyter Notebook will be executed.

-The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.

-You can also modify and re-run individual cells.

-For subsequent runs, choose “Runtime” and click “on “Restart and run all”.

-The Gurobi Optimizer will find the optimal solution of the modeling example.

Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.


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