amplpy#
AMPL - solve multiple models in parallel#
Description: Solve multiple AMPL models in parallel in Python with amplpy and the multiprocessing modules.
Author: Nicolau Santos (9 notebooks) <nicolau@ampl.com>
AMPL Bin Packing Problem with GCG#
Description: Dantzig-Wolfe decomposition for Bin Packing Problem with GCG
AMPL Capacitated p-Median Problem with GCG#
Description: Dantzig-Wolfe decomposition for Capacitated p-Median Problem with GCG
AMPL Christmas Model created by ChatGPT#
Description: Christmas model generated by ChatGPT
AMPL Development Tutorial 1/6 – Capacitated Facility Location Problem#
Description: This notebook marks the beginning of a six-part series.
AMPL Development Tutorial 2/6 – Stochastic Capacitated Facility Location Problem#
Description: This notebook continues our six-part series as the second installment.
AMPL Development Tutorial 3/6 – Benders Decomposition via AMPL scripting#
Description: In this third installment of our six-part series, we continue our exploration by addressing the complexities introduced by the stochastic programming formulation presented in part two.
AMPL Development Tutorial 4/6 – Benders Decomposition via PYTHON scripting#
Description: In this fourth installment of our six-part series, we advance our exploration by demonstrating how to adapt our AMPL script for use with AMPL’s Python API.
AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition#
Notebooks > AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition
Description: In the fifth installment of our six-part series, we delve deeper by showing how to evolve our Benders decomposition Python script from a serial execution to one that solves subproblems in parallel.
AMPL Development Tutorial 6/6 – Implementing Benders Decomposition with ampls#
Description: This concluding notebook in our six-part series delves into enhancing the efficiency of our decomposition algorithm by utilizing AMPL Solver Libraries (ampls).
AMPL Model Colaboratory Template#
Description: Basic notebook template for the AMPL Colab repository
Aircrew trainee scheduling with seniority constraints#
Description: Aircrew trainee scheduling with simpler seniority modeling
Tags: trainee-scheduling, aircrew-scheduling, employee-scheduling, seniority-constraints, seniority-ranking, preferential-bidding-system, multi-objective, lexicographic-objectives, amplpy
Author: Gleb Belov (9 notebooks) <gleb@ampl.com>
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
Containers scheduling#
Description: Scheduling model for harbor operations. It is a problem with dependences between containers, which should be dispatch the fastest possible. We are using the MP solver interfaces to model a complex system using techniques from Constraint Programming, such as indicator constraints, and logical or and forall operators. After the model is written, a couple instances are presented and Highs/Gurobi MIP solvers are used to tackle the problem.
Debugging Model Infeasibility#
Description: This notebook offers a concise guide on troubleshooting model infeasibility using AMPL’s presolve feature and other language capabilities.
Demand prediction and Optimization with scikit-learn & Amplpy#
Description: In this notebook, we will:
Diet and Other Input Models: Minimizing Costs#
Description: Diet case study, Chapter 2 from the AMPL book adapted to Python
Tags: amplpy, ampl-lecture
Diet model with Google Sheets#
Description: Diet model using Google Sheets
Dynamic routing example#
Description: Example of interactive optimization with GUI using AMPL and Google Maps
Author: Christian Valente (5 notebooks) <ccv@ampl.com>
Efficient Frontier with Google Sheets#
Description: Efficient Frontier example using Google Sheets
Author: Christian Valente (5 notebooks) <ccv@ampl.com>
Employee Scheduling Optimization#
Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.
Financial Portfolio Optimization with amplpy#
Description: Financial Portfolio Optimization with amplpy and amplpyfinance
Google Hashcode 2022#
Description: Google Hashcode 2022 Practice Problem
Hospitals-Residents MIP#
Description: hospitals-residents problem with ties problem solved with ampl and highs
Hydrothermal Scheduling Problem with Conic Programming#
Description: Hydrothermal Scheduling Problem using Second-Order Cones
Tags: amplpy, conic, second-order-cone, quadratic-cone, nonlinear-programming, scheduling, engineering, power-generation, geothermal-energy, hydropower
Author: Gleb Belov (9 notebooks) <gleb@ampl.com>
Introduction to Linear and Integer Programming#
Description: Basic introduction to linear programming and AMPL via a lemonade stand example
Introduction to Mathematical Optimization#
Description: Basic introduction to optimization and AMPL via unconstrained optimization
Jupyter Notebook Integration#
Description: Jupyter Notebook Integration with amplpy
Largest small polygon#
Description: lecture about models for the Largest Small Polygon Problem
Logistic Regression with amplpy#
Description: Logistic regression with amplpy using exponential cones
Multi-Objective Knapsack Problem with AMPLPY#
Description: knapsack problem using multiple objectives, setting objective-specific options
N-Queens#
Description: How can N queens be placed on an NxN chessboard so that no two of them attack each other?
Author: Gleb Belov (9 notebooks) <gleb@ampl.com>
NFL Team Rating#
Description: NFL Team Rating problem from the Analytical Decision Modeling course at the Arizona State University.
Network Linear Programs#
Description: Basic introduction to network linear programms and AMPL via max flow and shortest path problems
Optimal Power Flow with AMPL and Python - Bus Injection Model (BIM)#
Optimal Power Flow with AMPL and Python - Bus Injection Model (BIM) with controllable-phase shifting transformers and tap-changing transformers#
Optimal Power Flow with AMPL and Python - DC Power Flow#
Optimal Power Flow with AMPL and Python - conventional Power Flow#
Optimal Power Flow with AMPL and Python - data management#
Description: Optimal Power Flow with AMPL, Python and amplpy
Author: Nicolau Santos (9 notebooks) <nicolau@ampl.com>
Optimization Methods in Finance: Chapter 3#
Description: Optimization Methods in Finance: Bond Dedication Problem.
Optimize your Christmas Tree to Global Optimality#
Description: Optimize the placement of ornaments on a christmas tree.
Optimizing Procurement and Sales Strategies for a Retail Chain with Supplier Payment Schemes#
Optimizing the number of staff in a chain of stores#
P-Median problem#
Description: this notebook states the p-median problem with a simple example, and a MIP formulation in amplpy. The problem is parametrized with a class, so it is easier to sample and replicate experiments. A graphical solution is plotted.
Pattern Enumeration#
Description: Pattern enumeration example with amplpy
Pattern Generation#
Description: Pattern generation example with amplpy
Power System Optimization with Amplpower package#
Description: this notebook uses amplpower package to solver opf problems
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.
Pricing Optimization (Price Elasticity of Demand)#
Production Model: lemonade stand example#
Description: Basic introduction to AMPL’s indexed entities and the Pygwalker Python package via a lemonade stand example
Quick Start using Pandas dataframes#
Description: Quick Start using Pandas dataframes to load and retrieve data
Quick Start using lists and dictionaries#
Description: Quick Start using lists and dictionaries to load and retrieve data
Roll Cutting - Revision 1 & 2#
Description: Pattern tradeoff example with amplpy
Simple sudoku solver using logical constraints (with GUI)#
Description: Simple sudoku model with two formulations: as a Constraint Programming problem using the alldiff operator and as a MIP. Note that the CP formulation is more natural but it needs a solver supporting logical constraints or a MIP solver with automatic reformulation support (see [here](https://mp.ampl.com/) for more information).
Author: Christian Valente (5 notebooks) <ccv@ampl.com>
Solving simple stochastic optimization problems with AMPL#
Description: Examples of the Sample Average Approximation method and risk measures in AMPL
Author: Nicolau Santos (9 notebooks) <nicolau@ampl.com>
Sudoku Generator#
Description: Generate Sudoku boards with unique solution via iterative method and mip formulation.
Unit Commitment Problem with AMPL and Python - Power Grid Lib#
Description: Generic notebook to solve Unit Commitment problems with AMPL and Python using the Power Grid Lib model and test instances.
Author: Nicolau Santos (9 notebooks) <nicolau@ampl.com>
Unit Commitment for Colombia’s Energy Market#
Description: Unit Commitment and Reserve Co-Optimization in the Colombian Market.
Unit Commitment for Electrical Power Generation#
Description: This notebook illustrates the power generation problem using AMPL. The original version featured the Gurobi solver. By default, this notebook uses the HiGHS and CBC solvers.
Vehicle Routing Problem with Fair Profits and Time Windows (VRP-FPTW)#
amplpy setup & Quick Start#
Description: amplpy setup and quick start