About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
AAAI 2022
Workshop paper
End-to-End Learning via Constraint-Enforcing Approximators for Linear Programs with Applications to Supply Chains
Abstract
In many real-world applications, prediction problems are used to model forecast inputs for downstream optimization problems and it often suffices to check the performance of the final task-based objective, instead of intermediate task objectives, such as prediction error. The difficulty in end-to-end learning lies in differentiating through the optimization problem. Therefore, we propose a neural network architecture that can learn to approximately solve these linear programs, particularly ensuring its output satisfies the feasibility constraints. We further apply this to a multi-location newsvendor problem with cross fulfillment. We also analyze this problem with explicit fulfillment rules, and show the end-to-end problem can be solved with the exact derivative, without the need for approximations. We show that both these methods out-perform the predict following by optimize approach.