5/25/2023 0 Comments The Professor by Marie Q. Francois![]() ![]() ![]() It is founded on modern optimization and machine learning perspectives that encompasses developments in deep reinforcement/end-to-end learning, risk averse decision theory, and contextual/distributionally robust optimization. This research program aims at developing new methods for making the most effective and adaptive use of data in decision-making. Due to constantly evolving environments and the high frequency of data acquisition, classical decision-making that is based on training models, validating them, to finally optimize decisions does not suffice anymore. This is especially true in supply chains where, e.g., demand, cost, capacity, and travel time’s high variability considerably complicate the planning of procurement, production, distribution, and service activities. Nearly all decision problems involve some form of uncertainty. Lead researchers: Erick Delage (HEC Montréal, GERAD), Yossiri Adulyasak (HEC Montréal, GERAD), Emma Frejinger (Université de Montréal, CIRRELT) Topic 1 – Integrated Machine Learning and Optimization for Decision Making under Uncertainty: Towards Robust and Sustainable Supply Chains ![]()
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