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

Burcu Beykal (), Styliani Avraamidou (), Ioannis P. E. Pistikopoulos (), Melis Onel () and Efstratios N. Pistikopoulos ()
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Burcu Beykal: Texas A&M University
Styliani Avraamidou: Texas A&M University
Ioannis P. E. Pistikopoulos: Texas A&M University
Melis Onel: Texas A&M University
Efstratios N. Pistikopoulos: Texas A&M University

Journal of Global Optimization, 2020, vol. 78, issue 1, No 1, 36 pages

Abstract: 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.

Keywords: Data-driven modeling; Bi-level optimization; Global optimization; Grey-box optimization; Food-energy-water nexus (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10898-020-00890-3

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