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.
Tags: educational, bilevel, complementarity, amplpy, gurobi, knitro, baron, mpec, energy, electric-power-industry
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.
Formula 1 Scheduling and Routing Optimization#
Description: A notebook that tackles the Formula 1 Calendar as a routing and a scheduling problem, minimizing total distance between races whilst also assigning a spot in the calendar respecting scheduling constraints using MP
MineLib in AMPL and amplpy#
Description: A notebook that works with the open-pit mining problems defined in the MineLib library and shows how to read and parse the instances using Python and amplpy
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.
Unit Commitment MINLP with Knitro#
Description: Solving a nonlinear Unit Commitment problem with Knitro using MP features for logic and multi-objective optimization. The goal of this notebook is to show a straightforward and clear way of using nonlinear solvers for complex models with logical expressions and also hierarchical multi-objective optimization.
Tags: mp, knitro, mp2nl, nonlinear, quadratic, minlp, unit-commitment, electric-power-industry, energy, multi-objective, gurobi, xpress, mp2nl
Using multiple objectives in your model#
Description: We show how to use multiple objectives with Amplpy using a nonlinear Unit Commitment problem. We won’t be using native or emulated features from the solver interface, but emulating manually a lexicographic multiobjective problem.
Tags: warm-start, mp, multi-objective, gurobi, xpress, knitro, mp2nl, electric-power-industry, unit-commitment
Vehicle Routing Problem with Fair Profits and Time Windows (VRP-FPTW)#
Warm start solvers with snapshot#
Description: We show how to warm start a solver with a previous solution. A nonlinear Unit Commitment problem is being used as example. We will use the “snapshot” feature for this matter.