Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307509 
Year of Publication: 
2023
Citation: 
[Journal:] Optimization Letters [ISSN:] 1862-4480 [Volume:] 17 [Issue:] 5 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 1083-1103
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Bilevel problems are used to model the interaction between two decision makers in which the lower-level problem, the so-called follower's problem, appears as a constraint in the upper-level problem of the so-called leader. One issue in many practical situations is that the follower's problem is not explicitly known by the leader. For such bilevel problems with unknown lower-level model we propose the use of neural networks to learn the follower's optimal response for given decisions of the leader based on available historical data of pairs of leader and follower decisions. Integrating the resulting neural network in a single-level reformulation of the bilevel problem leads to a challenging model with a black-box constraint. We exploit Lipschitz optimization techniques from the literature to solve this reformulation and illustrate the applicability of the proposed method with some preliminary case studies using academic and linear bilevel instances.
Subjects: 
Bilevel optimization
Unknown follower problems
Neural networks
Lipschitz optimization
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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