gurobi (33 notebooks)#

AMPL Bin Packing Problem with GCG#

bpp.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Dantzig-Wolfe decomposition for Bin Packing Problem with GCG

AMPL Christmas Model created by ChatGPT#

AMPL Development Tutorial 6/6 – Implementing Benders Decomposition with ampls#

6_benders_ampls_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

Balanced Task Assignment with Inverse Cost Scaling#

Book Example: Economic equilibria#

economic_eq_lecture.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: economic model using complementarity conditions from Chapter 19 AMPL book

Containers scheduling#

containers_scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

Employee Scheduling Optimization#

Employee_Scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

Notebook_3_Porfolio_Optimization_Sector_ETF.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: This notebook compares multiple portfolio optimization strategies for invesment in Sector ETFs

Financial Portfolio Optimization with amplpy#

amplpyfinance_vs_amplpy.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Financial Portfolio Optimization with amplpy and amplpyfinance

Introduction to Linear and Integer Programming#

intro_to_linear_prorgramming.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to linear programming and AMPL via a lemonade stand example

Introduction to Mathematical Optimization#

intro_to_optimization.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to optimization and AMPL via unconstrained optimization

Jupyter Notebook Integration#

magics.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Jupyter Notebook Integration with amplpy

NFL Team Rating#

NFL_Team_Rating.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: NFL Team Rating problem from the Analytical Decision Modeling course at the Arizona State University.

Network Linear Programs#

network.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to network linear programms and AMPL via max flow and shortest path problems

Network design with redundancy#

electric_grid_with_redundancy.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

Optimization of Reinforced Concrete Production and Shipment: A Conveyor-Based Manufacturing and Curing Model#

Optimize your Christmas Tree to Global Optimality#

Optimized Portfolio Optimization using EIA Data in Python with AMPL#

Notebook_1_Portfolio_Optimization_Commodities.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

pattern_enumeration.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern enumeration example with amplpy

Pattern Generation#

pattern_generation.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern generation example with amplpy

Porfolio Optimization with Multiple Risk Strategies in Python with AMPL#

Notebook_4_Porfolio_Optimization_Risk_Strategies.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

Pricing Optimization (Price Elasticity of Demand)#

Production Model: lemonade stand example#

production_model.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

solution_pool.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

tip6_robust_linear_programming.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: AMPL Modeling Tips #6: Robust Linear Programming

Roll Cutting - Revision 1 & 2#

pattern_tradeoff.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern tradeoff example with amplpy

Scheduling Multipurpose Batch Processes using State-Task Networks in Python#

batch_processessing.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

Smart Pipeline Diagnostics#

amplpy setup & Quick Start#

quickstart.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: amplpy setup and quick start