INMAS 2021: Modeling and Optimization

This is the webpage for the Modeling and Optimization portion of Inmas 2021. All material for the course will be posted here.

Instructor: Alex Estes
email: este0100@umn.edu

TAs:
Nik Wojtalewicz
email: npw2@illinois.edu

Vaibhav Karve
email: vkarve2@illinois.edu

Overview

This workshop introduces optimization models and methods for solving decision-making problems in areas such as engineering, business, economy, manufacturing, finance and healthcare. The ultimate goal is to teach students how to solve large-scale optimization problems from the real world. The course will introduce the following topics:

• Formulating decision problems into mathematical optimization models.
• Transforming an optimization model into a format that is compatible with optimization solvers (e.g. Gurobi and CPLEX).
• The capability and limitations of a variety of optimization solver software.
• Basic principles of some optimization algorithms.

Pre-workshop Preparations

Prior to the workshop, install Gurobi and a Python distribution and configure your Python distribution so that Gurobi works with it. The examples that I give will use Python 3, so I recommend using Python 3. Otherwise, you might have to make some modifications to get the examples to work. Gurobi provides quickstart reference guides (https://www.gurobi.com/documentation/quickstart.html). Follow the quickstart guide for your operating system. The first step of the quickstart guide will direct you to a obtain a Gurobi license; follow the directions for the free academic license. In order to confirm that you have set up Gurobi correctly, run one of the example scripts provided by Gurobi (examples can be found in <installdir>/examples/python). I also recommend that you read the introduction to mathematical programming available on the Gurobi website (http://www.gurobi.com/resources/getting-started/modeling-basics).

If you run into any problems in any of these steps, feel free to reach out to Alex Estes. There are a lot of steps involved, and it’s easy to get lost. If you run into issues, make sure you have completed all of the following:

1. Download and install Gurobi (links: windows linux mac). If you are using Linux, remember to update your configuration files as described in the link.
2. Request an academic license (links: windows linux mac)
3. Associate the academic license with your computer (links: windows linux mac). This will require an active internet connection. In this step, Gurobi checks that your IP address is registered with an academic institution. If you are not on a university network, you may have to connect to a university network via VPN to carry out this step. If you place the license file in a non-default location, remember to update your environmental variables (links: windows linux mac)
4. Install the python distribution and/or IDE of your choice (for example, Anaconda)
5. Install Gurobi into your python distribution (links: windows linux mac)

Workshop Schedule

Zoom meeting link: click here

All following times are in Central time.
Session 1: Friday, April 23rd, 2:00 p.m. to 5:00 p.m.

• Slides: click here.
• Gurobi examples: piecontest.py, prodplanning_linexpr.py, productionplanning.py
• Problems: click here, solutions, fourth_moment_max.py, convex_hull_intersection.py

Session 2: Saturday, April 24th, 9:00 a.m. to 12:00 p.m.

• Slides: click here.
• Problems: click here, solutions, graphcolor.py, machineschedule.py

Session 3: Saturday, April 24th, 2:00 p.m. to 5:00 p.m.

• Slides: click here.
• Problems: click here, solutions

Session 4: Sunday, April 25th, 9:00 a.m. to 12:00 p.m.

• Slides: click here.
• Gurobi examples: tsp.py
• Problems: click here

Further reading:

Some good resources on optimization:

• Walker, R. (1999). Introduction to Mathematical Programming. New Jersey: Prentice Hall.
• Bertsimas D. and Tsitsiklis J.N. (1997). Introduction to Linear Optimization. Belmont, MA: Athena Scientific.
• Nemhauser, G.L. and Wolsey L. (1999). Integer and Combinatorial Optimization. John Wiley and Sons.
• Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
• Nocedal, J. and Wright, S. (2006). Numerical Optimization. Springer Science and Business Media.
• Birge, J. and Louveaux F. (2011). Introduction to Stochastic Programming (2nd edition). Springer Science and Business Media.
• Papadimitriou, C.H. and Seiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity. Dover.