Presentation on YouTube.

In order to make sense of future uncertainty, managers have long resorted to creating scenarios that are then used to evaluate how uncertainty affects decision-making. The large number of scenarios that are required to faithfully represent several sources of uncertainty leads to major computational challenges in using these scenarios in a decision-support context. Moreover, the complexity induced by the large number of scenarios can stop decision makers from reasoning about the interplay between the uncertainty modelled by the data and the decision-making processes (i.e., how uncertainty affects the decisions to be made). To meet this challenge, we propose a new clustering approach to group scenarios based on the decisions associated to them. In this talk, I will present the developed scenario clustering approach and show how it can be used to derive efficient bounds for stochastic optimization models.

Short bio: Walter Rei is a Professor of Operations Research at the Department of Analytics, Operations, and Information Technologies of the École des Sciences de la Gestion, Université du Québec à Montréal, Canada. He currently holds the Canada Research Chair in Stochastic Optimization of Transport and Logistics Systems and he is also a member of the Interuniversity Research Centre on Enterprise Networks,Logistics and Transportation (CIRRELT). His research interests center on the development of efficient solution methodologies for integer stochastic programs and combinatorial optimization models relevant to transportation and logistics problems.