# Marcos Dominguez Velad (23 notebooks)#

## Book Example: Economic equilibria#

Description: economic model using complementarity conditions from Chapter 19 AMPL book

## Book Example: Transshipment problem#

Description: book example with general transshipment model (net1.mod)

## Book Example: diet#

Description: book example autogenerated using diet.mod, diet.dat, and diet.run

## 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.

## Diet lecture#

Description: Diet case study

Tags: ampl-only, ampl-lecture

## Employee Scheduling Optimization#

Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.

## Google Hashcode 2022#

Description: Google Hashcode 2022 Practice Problem

## Hospitals-Residents MIP#

Description: hospitals-residents problem with ties problem solved with ampl and highs

## Labs scheduling#

Description: Model for laboratories scheduling. Some labs are needed to handle requests from researchers, and departments have to assign labs and locations to the requests.

## Largest small polygon#

Description: lecture about models for the Largest Small Polygon Problem

## Magic sequences#

Description: Solving magic sequences through reinforced formulations and constrained programming. Some comparison between models and solvers is done, and we look into the “Another solution” problem for these sequences.

Tags: constraint-programming, educational, mp, sequences, arithmetic, reinforced-formulations, highs, gecode, cbc, mip

## Multicommodity transportation problem#

Description: Multicommodity transportation model with binary variables

## NFL Team Rating#

Description: NFL Team Rating problem from the Analytical Decision Modeling course at the Arizona State University.

## Nonlinear transportation model#

Description: book example autogenerated using nltransd.mod, nltrans.dat, and nltrans.run

## Nonlinear transportation problem example#

Description: book example autogenerated using nltransd.mod, nltrans.dat, and nltrans.run

## Optimization Methods in Finance: Chapter 3#

Description: Optimization Methods in Finance: Bond Dedication Problem.

## 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.

## Production model#

Description: generic model for production problem

## Steel industry problem#

Description: model for steel production problem

## Sudoku Generator#

Description: Generate Sudoku boards with unique solution via iterative method and mip formulation.

## Supply chain network#

Description: Compute optimal routes to connect suppliers/demanding nodes in a network. Routes have an associated fixed and variable cost. There are different products to ship. The problem is formulated as a MIP with binary variables. Python data structures are used to load the data into the model.

## Transportation problem#

Description: an AMPL model for the transportation problem

Tags: ampl-only, ampl-lecture

## Warehouse location and transport#

Description: Model for warehouse allocation. Farms (suppliers) send feedstock to warehouses, and later on, those warehouses send it to a production plant. The problem involves modeling a storage facility location problem with a transportation component to the final plant.