Robust decision making

We are living in a world with lots of uncertainties. Weather conditions, prices, and traffic jams are only a few uncertainties that we are encountering in our daily life. Societal and industrial parties are also facing uncertainties in demands, costs, and many more parameters on which their revenues are based. So, there are two main questions:
(i) how sensitive is the solution obtained using the customary modeling approaches?
(ii) how can we obtain a solution that is not sensitive (or robust) against uncertainties?

The goal of this course is to teach students how to answer these questions. We are going to cover the following topics

• Preknoledge on Duality theories in Linear Optimization problems

• Sensitivity Analysis based on dual solutions

• Static Robust Optimization (SRO), as a method to obtain robust solutions

• Adjustable Robust Optimization (ARO), as a method to obtain robust policies

• Applications of SRO and ARO in Operations Management problems

Additional information test
Procedure in case of insufficient grade:

The assessments of each of the subcases together form the final grade for this course. 
Students who got insufficient score for the course (<5.5) can use the retake option, where they get a new sub-case. The score of this sub-case will replace the score of the sub-case with the least score. The highest possible grade after taking the resit case for the course is 6. So, if a student gets 4, 5, 5, and 6, as the scores of the four sub-cases, they get a total score of 5. If the student takes the retake option and gets 6, then the final score is;

6+ 5+ 5 + 6 = 5.5
           4

The goal of the course is to teach students how to deal with uncertainties in an Industrial Engineering problem. Students who pass this course are able to:

• Identify existence of an uncertainty in an Industrial Engineering problem
• Formulate dual problems of a linear optimization problem
• Identify sensitivities of a solution to different uncertainties
• Recognize different methods to obtain robust policies for practical problems
• Formulate proper Robust Optimization model based on a problem description
• Implement Robust Optimization models into Python
• Solve Robust Optimization models to obtain robust policies

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Completed none of the course modules listed below
• Robust policies for OM (1CM220)
• Collection 2
• Completed Pre-Master
• Completed none of the course modules listed below
• Robust policies for OM (1CM220)

• Book “Robust and adaptive optimization” by Bertsimas D, den Hertog, D, 2022
• Papers Links to the papers will be provided via Canvas