mip-incentive-allocation

README

Nextmv MIP incentive allocation template

This template demonstrates how to solve a Mixed Integer Programming problem using the open-source solver HiGHS. To run this beta template, please contact support@nextmv.io for access to the Nextmv HiGHS interface.

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 a incentive allocation problem.

Given a collection of incentives per user with a cost and effect, our objective is to maximize the total effect without exceeding a given budget.

The input defines a number of users which have an id to identify them, and a set of in incentives per user with a cost, an effect and a name. There is also an available budget.

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

• main.go implements a MIP incentive allocation solver.
• input.json is a sample input file that follows the input definition in main.go.

Run the command below to see if everything works as expected:

nextmv sdk run main.go -- -runner.input.path input.json \
  -runner.output.path output.json -limits.duration 10s

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

Next steps

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

Documentation

Overview

package main holds the implementation of the mip incentive allocation template.