gurobi#
Balanced Task Assignment with Inverse Cost Scaling#
Bilevel Markets#
Description: A notebook that presents a comprehensive mathematical formulation of strategic bidding in electricity markets using bilevel optimization and its equivalent single-level Mathematical Program with Equilibrium Constraints (MPEC) obtained through Karush-Kuhn-Tucker (KKT) transformation.
Bilevel Optimization Introduction#
Description: A notebook as a gentle introduction to Bilevel Optimization and how to reformulate to Single Level using KKT conditions and Complementarity using AMPLPy and MP with a simple Stackelberg model
Book Example: Economic equilibria#
Description: economic model using complementarity conditions from Chapter 19 AMPL book
Demand prediction and Optimization with scikit-learn & Amplpy#
Description: In this notebook, we will:
Employee Scheduling Optimization#
Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.
NFL Team Rating#
Description: NFL Team Rating problem from the Analytical Decision Modeling course at the Arizona State University.
Optimizing the number of staff in a chain of stores#
Predicting and Optimizing Avocado Sales with Python + Amplpy#
Description: In this notebook, we explore a real-world example of demand estimation and supply optimization using a Kaggle dataset on avocado sales. We start by training a machine learning model to estimate demand and then formulate and solve an optimization model in AMPL to maximize revenue while minimizing waste and transportation costs.
Retrieve Solution pool with AMPL and Gurobi#
Description: This notebook describes how to retrieve multiple solutions from the solver’s solution pool. Optimization problems usually have several optimal solutions, one is returned by the solver but the others are discarded. These alternative solutions can also be retrieved by AMPL.