# gurobi (22 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

## 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.

## 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

## 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

## 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.

## Optimize your Christmas Tree to Global Optimality¶

Description: Optimize the placement of ornaments on a christmas tree.

## Pattern Enumeration¶

Description: Pattern enumeration example with amplpy

## Pattern Generation¶

Description: Pattern generation example with amplpy

## Production Model¶

Description: Basic introduction to AMPL’s indexed entities and the Pygwalker Python package via a lemonade stand example

## Robust Linear Programming with Ellipsoidal Uncertainty¶

Description: AMPL Modeling Tips #6: Robust Linear Programming

## 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.

## amplpy setup & Quick Start¶

Description: amplpy setup and quick start