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Nextmv Xpress Python template

This template demonstrates how to solve a Mixed Integer Programming problem using the commercial solver FICO Xpress.

To solve a Mixed Integer Problem (MIP) is to optimize a linear objective function of many variables, subject to linear constraints. We demonstrate this by solving the knapsack problem using the Python interface.

Knapsack is a classic combinatorial optimization problem. Given a collection of items with a value and weight, our objective is to maximize the total value without exceeding the weight capacity of the knapsack.

The input defines a number of items which have an id to identify the item, a weight and a value. Additionally there is a weight capacity.

The most important files created are main.py and input.json.

  • main.py implements a MIP knapsack solver.
  • input.json is a sample input file.

Follow these steps to run locally.

  1. The packages listed in the requirements.txt will get bundled with the app as defined in the app.yaml manifest. When working locally, make sure that these are installed as well:

    pip3 install -r requirements.txt

  2. Run the command below to check that everything works as expected:

    python3 main.py -input input.json -output output.json -duration 30

  3. A file output.json should have been created with the optimal knapsack solution.

Mirror running on Nextmv Cloud locally

Pre-requisites: Docker needs to be installed.

To run the application locally in the same docker image as the one used on the Nextmv Cloud, you can use the following command:

cat input.json | docker run -i --rm \
-v $(pwd):/app ghcr.io/nextmv-io/runtime/python:3.11 \
sh -c 'pip install -r requirements.txt > /dev/null && python3 /app/main.py'

You can also debug the application by running it in a Dev Container. This workspace recommends to install the Dev Container extension for VSCode. If you have the extension installed, you can open the workspace in a container by using the command Dev Containers: Reopen in Container.

Next steps

  • Open main.py and read through the comments to understand the model.
  • Further documentation, guides, and API references about custom modeling and deployment can also be found on our blog and on our documentation site.
  • Need more assistance? Send us an email!