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ISyE Department Seminar - Andrea Lodi

Dealing with uncertainty in tactical planning by machine learning.

In this talk, we propose a methodology to predict descriptions of solutions to discrete stochastic optimization problems in very short computing time. We approximate the solutions based on supervised learning and the training dataset consists of a large number of deterministic problems that have been solved independently (and offline). Uncertainty regarding a subset of the inputs is addressed through sampling and aggregation methods. Our motivating application concerns booking decisions of intermodal containers on doublestack trains. Under perfect information, this is the so-called load planning problem and it can be formulated by means of integer linear programming. However, the formulation cannot be used for the application at hand because of the restricted computational budget and unknown container weights. The results show that standard deep learning algorithms allow to predict descriptions of solutions with high accuracy in very short time (milliseconds or less). A careful comparison with alternative stochastic programming approaches is provided.

Event Details

Wednesday, 10 April 2019 - 1:30pm to 2:30pm

ISyE Main Room 228

ISyE location map

Georgia Tech Supply Chain and
Logistics Institute
H. Milton Stewart School of
Industrial & Systems Engineering
765 Ferst Drive, NW, Suite 228
Atlanta, GA 30332
Phone: 404.894.2343