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Publication Detail

Title: DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems.

Authors: Beykal, Burcu; Avraamidou, Styliani; Pistikopoulos, Ioannis P E; Onel, Melis; Pistikopoulos, Efstratios N

Published In J Glob Optim, (2020 Sep)

Abstract: The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer nonlinear programming problems. In this framework, bi-level optimization problems are approximated as single-level optimization problems by collecting samples of the upper-level objective and solving the lower-level problem to global optimality at those sampling points. This process is done through the integration of the DOMINO framework with a grey-box optimization solver to perform design of experiments on the upper-level objective, and to consecutively approximate and optimize bi-level mixed-integer nonlinear programming problems that are challenging to solve using exact methods. The performance of DOMINO is assessed through solving numerous bi-level benchmark problems, a land allocation problem in Food-Energy-Water Nexus, and through employing different data-driven optimization methodologies, including both local and global methods. Although this data-driven approach cannot provide a theoretical guarantee to global optimality, we present an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved to global optimality at convergence.

PubMed ID: 32753792 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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