The notebooks in the exercises folder can be downloaded and run locally if you have Python installed.
Alternatively, you can run each exercise on Google Colab, a free online platform for coding exercises. You will need to be logged in to a google account in your browser.
Using the links below will open a fresh copy of the notebook to work on - your changes will not be visible to anyone else. However, if you want to be able to refer back to your version of the notebook in future, make sure you click 'File --> Save to Drive'. Your changes will then be saved to your own account, and you can access your edited copy of the notebook from https://colab.research.google.com/.
Exercise 1 is a short exercise without Python.
Open Exercise 2 in Google Colab:
Notebooks are split into core, extension and challenge sections.
All students should aim to complete the exercises within the core section. Completing these exercises will give you practice of all of the key concepts discussed in the lectures and you can stop after this section if you wish.
Students looking to push themselves and their understanding can go on to attempt the extension exercises if they would like to.
The challenge section contains exercises that may go beyond what is covered in the lectures; there will be an expectation of looking things up in documentation or on sites such as StackOverflow, or using tools such as perplexity.ai to obtain boilerplate code. These exercises may take significantly longer than is allocated during the lectures and are designed to be an enjoyable challenge for those who want to push their coding skills.
Solution notebooks are available in the solutions folder, or can be opened in Colab.
Open Exercise 2 SOLUTION in Google Colab:
Students should be able to:
- Explain the difference between p-median facility location problems, maximal covering location problems (MCLP), and location set covering problems (LSCP)
- Code a brute-force solution to a small-scale p-median optimization problem
- Evaluate and visualise solutions to small-scale p-median optimization problems
- Explain why it is not possible to evaluate every solution in larger optimization problems
- Explain the benefits and limitations of using random restarts on larger problems
- Explain the high-level principles of evolutionary algorithms for tackling larger-scale optimisation problems