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

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...

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Bibliographic Details
Published in:Journal of global optimization Vol. 78; no. 1; pp. 1 - 36
Main Authors: Beykal, Burcu, Avraamidou, Styliani, Pistikopoulos, Ioannis P. E., Onel, Melis, Pistikopoulos, Efstratios N.
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2020
Springer
Springer Nature B.V
Subjects:
Computer Science
Design of experiments
Design optimization
Mathematics
Mathematics and Statistics
Mixed integer
Nonlinear programming
Operations Research & Management Science
Operations Research/Decision Theory
Optimization
Real Functions
Data-driven modeling
Global optimization
Bi-level optimization
Food-energy-water nexus
Grey-box optimization
ISSN:0925-5001, 1573-2916
Online Access:Get full text
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Summary: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.
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EE0007888
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-020-00890-3