Robust optimization is a partly young and active research field and has been mainly developed in the last 25 years. There have been many publications that show the value of robust optimization in applications in many fields including finance, management science, supply chain, healthcare, engineering, etc.
Like all other areas of optimization, in robust optimization the theoretical parts of each research project should be completed by the experimental ones. More importantly, researchers should have the ability to compare the similar algorithms to better understand their value. To this end, it is necessary to collect different types of instances in a library so that all researchers working in the field of robust optimization can easily have access to them.
In this website, we have been working to collect the instances which are used in different publication and if the instances are not accessible, we regenerate them exactly based on their original description. Furthermore, in order to ease of usability we categorize the problems on the instance page based on four factors called problem, uncertainty, criterion and type.