ampl#

AMPL - solve multiple models in parallel#

multiproc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Solve multiple AMPL models in parallel in Python with amplpy and the multiprocessing modules.

AMPL - spreadsheet handling with amplxl#

amplxl.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic example of reading/writing data into/from a .xlsx spreadsheet with amplxl

AMPL Development Tutorial 1/6 – Capacitated Facility Location Problem#

AMPL Development Tutorial 2/6 – Stochastic Capacitated Facility Location Problem#

AMPL Development Tutorial 3/6 – Benders Decomposition via AMPL scripting#

3_benders_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

4_benders_in_python_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

5_benders_parallel_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

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).

Debugging Model Infeasibility#

debug_infeas.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: This notebook offers a concise guide on troubleshooting model infeasibility using AMPL’s presolve feature and other language capabilities.

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

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

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#

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

Production Model#

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: Minimizing the cost of implementing an investment project, taking into account the costs and risks of completing tasks and penalties for late fulfillment of obligations.#

Solving simple stochastic optimization problems with AMPL#

newsvendor.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Examples of the Sample Average Approximation method and risk measures in AMPL