crrodger/nd-pyomo-cookbook

A repository of Pyomo examples.

ND-Pyomo-Cookbook

Pyomo is a state-of-the-art Python package for modeling and solving optimization problems. Using Pyomo, a user can embed an optimization model consisting of decision variables, constraints, and an optimization objective within Python. Pyomo includes a rich set of features to enable modeling of complex systems, specifying a solver, and accessing the solution.

This repository provides instructions on getting started with Pyomo, and a collection of Pyomo modeling notebooks that have been developed for instructional purposes at Notre Dame. The notebooks were originally developed using the Anaconda distribution of Python. The notebooks have been recently updated to open directly on Google Colaboratory which enables their using only a browser window.

PyomoFest at Notre Dame was held June 5-7, 2018. This repository contains the agenda, slides and exercises distributed during that event.

Getting Started
Running Pyomo on the CRC Cluster (ND specific instructions)
Running Pyomo on Google Colab
Unconstrained Scalar Optimization

Linear Programming
Production Models with Linear Constraints
Linear Blending Problems
Mixture Design for a Cold Weather Camping Fuel
Simple Gasoline Blending Example

Assignment Problems
Transportation Networks

Scheduling Problems with Disjunctive Constraints
Machine Bottleneck
Job Shop Scheduling
Scheduling Multipurpose Batch Processes using State-Task Networks

Simulation
Linear First Order System
Simulation of an Exothermic Stirred Tank Reactor
Heat Conduction in Various Geometries

Differential Algebraic Equations
Maximizing Concentration of an Intermediate in a Batch Reactor
Path Planning for a Simple Car Model

Parameter Estimation
Parameter Estimation for a Catalytic Reactor

Finance
Binomial Model for Pricing Options
Historical Stock Data *
Charting Stock Data *
MAD Portfolio Optimization
Real Options

• Notebooks require access to stock data services.