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Nextmv Python AMPL Price Optimization

Example for running a Python application on the Nextmv Platform using the AMPL package. We solve a price optimization Mixed Integer Problem (MIP). This app is inspired by the Avocado Price Optimization blog post published by Gurobi.

We aim to optimize both the price and quantity (in millions) of a product shipped to a set of regions. The revenue in each region is determined by the sales volume and the price of the product. The sales volume is influenced by the price and it cannot exceed the quantity of the product supplied to the region.

The cost of supplying a product to each region includes the cost of waste (unsold products) and the cost of transport.

Given a set of regions, a total supply of product, minimum / maxium product allocations and prices per region, costs to transport products and costs for wasted products, and regression coefficients for a model correlating price to expected demand, we determine the following:

  • price (price of the product in each region)
  • quantity (quantity of the product supplied to each region)

While maximizing expected profit (revenue - cost).

If you have an AMPL license, remove the .template extension from the ampl_license_uuid.template file and replace the contents with your actual license key. Modify the app.yaml file to include the ampl_license_uuid in the files list.

  1. Install packages.

    pip3 install -r requirements.txt

  2. Run the app.

    python3 main.py -input input.json -output output.json \
  -duration 30 -provider highs -model .

Mirror running on Nextmv Cloud locally

Docker needs to be installed.

To run the application in the same Docker image as the one used on 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 modify the model.
  • Visit our docs and blog. Need more assistance? Contact us!

Notes

This model rounds off some variables for simplicity. We recommend users handle data types explicitly when working with this model.