hwiberg/OptiCL
An end-to-end framework for mixed-integer optimization with data-driven learned constraints.
This framework helps you optimize decisions when you have an objective and some constraints, but the outcomes aren't easily described by formulas. You provide historical data showing how different decisions led to various outcomes, along with your initial understanding of the problem. OptiCL then learns from this data and automatically generates an optimization model, providing you with the best decisions to achieve your goals. This is for operations managers, supply chain planners, or anyone making strategic decisions based on data rather than predefined equations.
140 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to optimize decisions for an objective or constraints where the relationship between your decisions and outcomes is complex and best understood through historical data, not simple formulas.
Not ideal if you already have clear, mathematical functions defining all your objectives and constraints, or if you primarily need to predict outcomes without an optimization step.
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140
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21
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 04, 2023
Commits (30d)
0
Dependencies
5
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