This publication concerns the overview and lecture notes for the course Robust policies for operations management problems.

For all other materials, please check the resources (part 1-5) and the links to the web lectures.

Societal and industrial parties are facing uncertainties in demands, costs, and many more parameters on which their revenues based. So, the important question is: how companies are making decisions while many of the decision parameters are uncertain?

In this resources related to this course we will learn how to answer this question from the perspective of “robustness". This is concept that helps us to make decisions that are not vulnerable to the changes in the uncertain parameters.

So, most important concept that will be discussed is: Robustness in Optimization problems.

Robustness is critical, especially in making strategic and tactical decisions, which are the decisions that are optimal for a long time.
Furthermore, it is important to know how the companies should react to the realization of an uncertain parameter. For instance, what companies should do when the demand is known, the prices are set, the weather condition is known, and etcetera.

Learning objectives:

• Different ways of formulating optimization problems
• Robust decisions methods to solve or approximate the formulated optimization problems
• Apply these methods to Operation Management problems and real-life cases.

This course consists of 5 modules:

-Module 1: Introduction and motivation
1.A: Lecture: Introduction
1.B: Lecture Motivation

-Module 2: Static robust optimization
2.A: Robust Conterpart
2.B: Vertex enumeration
2.C: Robust Facility Location
2.D: Uncertainty set Selection

-Module 3: Adaptive robust optimizationo What the idea behind Finite Adaptibility is and how to formulate it mathematically.
3.A: Adaptivity in optimization - Adaptive robust optimization - •Difference with (static) RO
3.B: Finitely adaptability
3.C: Affine decision rules
3.D: Finite scenario approach

-Module 4: Robust optimization with integer variable
4.A: Lecture
4.B: Excercise and solutions

-Module 5: Art of modeling