DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems. Academic Article uri icon


  • 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.

published proceedings

  • J Glob Optim

author list (cited authors)

  • Beykal, B., Avraamidou, S., Pistikopoulos, I., Onel, M., & Pistikopoulos, E. N.

citation count

  • 14

complete list of authors

  • Beykal, Burcu||Avraamidou, Styliani||Pistikopoulos, Ioannis PE||Onel, Melis||Pistikopoulos, Efstratios N

publication date

  • September 2020