Overview

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March 23-27, 2020
Optimization under uncertainty

In organizational decision making, the vast majority of planning and operational decisions are made without a complete knowledge of both the current situation and the future. Plans that are made when part of the contextual information is uncertain are then adjusted when new information is revealed, thus allowing for recourse actions to be taken. In general, this leads to problems where sequences of decisions are made over varying time horizons and where one aims to balance both the immediate benefits of the decisions made and their future impact. For such problems, designing optimization methodologies that explicitly consider the various sources of uncertainty that are present, and efficiently produce high-quality solutions, is quite challenging. This being said, today's access to more detailed and exhaustive databases detailing the activities of organizations, governments and populations, in addition to machine learning techniques being more readily available to analyze such databases, has greatly improved the operations research (OR) community's capacity to both describe the various sources of uncertainty more accurately and calculate the effects that the different realizations might have on the expected performance. Furthermore, driven by the advancements in both computing technologies and OR tools, one observes an ever-increasing amount of research dedicated to the development of specialized optimization methodologies capable of solving problems in realistic settings. The purpose of this workshop is to present some of the most recent and innovative technical developments in three important fields of study: stochastic combinatorial optimization, Monte Carlo methods, and robust optimization.

Registration fees:

(Places are limited, register before March 16, 2020)

-Students: $ 75

-Postdoctoral fellows: $115

-Faculty without grant: $150

-Faculty with grant: $220

-Industrial researchers: $380