gurobi (44 notebooks)#
AMPL Bin Packing Problem with GCG#
Description: Dantzig-Wolfe decomposition for Bin Packing Problem with GCG
AMPL Christmas Model created by ChatGPT#
Description: Christmas model generated by ChatGPT
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).
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
Book Example: Economic equilibria#
Description: economic model using complementarity conditions from Chapter 19 AMPL book
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.
Demand prediction and Optimization with scikit-learn & Amplpy#
Description: In this notebook, we will:
Diagnose infeasibility#
Description: This notebook demonstrates how to deal with infeasible models.
Employee Scheduling Optimization#
Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.
Enhanced Sector ETF Portfolio Optimization with Multiple Strategies in Python with AMPL#
Description: This notebook compares multiple portfolio optimization strategies for invesment in Sector ETFs
Tags: finance, portfolio-optimization
Financial Portfolio Optimization with amplpy#
Description: Financial Portfolio Optimization with amplpy and amplpyfinance
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
Multi-Objective Knapsack Problem with AMPLPY#
Description: knapsack problem using multiple objectives, setting objective-specific options
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
Network design with redundancy#
Description: Design of an electricity transportation network provides enough redundancy, so that a break of one component does not prevent any user from receiving electricity. The approach also works for similar distribution networks and can potentially be used in the design of military logistic networks.
Tags: electric-grid, military
Optimization of Reinforced Concrete Production and Shipment: A Conveyor-Based Manufacturing and Curing Model#
Optimize your Christmas Tree to Global Optimality#
Description: Optimize the placement of ornaments on a christmas tree.
Optimized Portfolio Optimization using EIA Data in Python with AMPL#
Description: Portfolio Optimization across Crude Oil, Gold, Natural Gas, Silver, and the S&P 500.
Optimizing Procurement and Sales Strategies for a Retail Chain with Supplier Payment Schemes#
Optimizing the number of staff in a chain of stores#
Pattern Enumeration#
Description: Pattern enumeration example with amplpy
Pattern Generation#
Description: Pattern generation example with amplpy
Porfolio Optimization with Multiple Risk Strategies in Python with AMPL#
Description: This notebook evaluates three distinct risk-based portfolio strategies: Semivariance Optimization, Conditional Value-at-Risk (CVaR) Optimization, and Conditional Drawdown-at-Risk (CDaR) Optimization.
Portfolio Optimization: Factor Model#
Description: Mean-Variance Portfolio Optimization model where the risk estimator is not given explicitly but is instead represented by a factor model, as is common in US equity models [1]. The original notebook is [3].
Tags: finance, portfolio-optimization, mean-variance, factor-model, ampl-conditonal-instantiation, cardinality-constraint
Author: Gleb Belov (9 notebooks) <gleb@ampl.com>
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
Project management: Minimize project costs by balancing task costs, risks, and late penalties.#
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.
Robust Linear Programming with Ellipsoidal Uncertainty#
Description: AMPL Modeling Tips #6: Robust Linear Programming
Author: Gleb Belov (9 notebooks) <gleb@ampl.com>
Roll Cutting - Revision 1 & 2#
Description: Pattern tradeoff example with amplpy
Scheduling Multipurpose Batch Processes using State-Task Networks in Python#
Description: The State-Task Network (STN) is an approach to modeling multipurpose batch process for the purpose of short term scheduling. It was first developed by Kondili, et al., in 1993, and subsequently developed and extended by others.
Author: Jeffrey C. Kantor, Filipe Brandão (16 notebooks) <fdabrandao@gmail.com>
Smart Pipeline Diagnostics#
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.
Vehicle Routing Problem with Fair Profits and Time Windows (VRP-FPTW)#
amplpy setup & Quick Start#
Description: amplpy setup and quick start