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

Burcu Beykal1,2, Styliani Avraamidou1,2, Ioannis P E Pistikopoulos1,2

  • 1Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA.

Journal of Global Optimization : an International Journal Dealing with Theoretical and Computational Aspects of Seeking Global Optima and Their Applications in Science, Management and Engineering
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

The DOMINO framework optimizes complex bi-level mixed-integer nonlinear problems by approximating them as single-level problems. This data-driven approach enhances feasibility for large-scale problems, even without guaranteed global optimality.

Keywords:
Bi-level optimizationData-driven modelingFood-energy-water nexusGlobal optimizationGrey-box optimization

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Area of Science:

  • Operations Research
  • Computational Optimization
  • Applied Mathematics

Background:

  • Bi-level mixed-integer nonlinear programming (MINLP) problems are prevalent in complex decision-making scenarios.
  • Exact methods often struggle with the computational complexity of large-scale bi-level MINLP problems.
  • Data-driven approaches offer alternative strategies for tackling these challenging optimization tasks.

Purpose of the Study:

  • To introduce the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework.
  • To approximate bi-level MINLP problems as single-level problems using data-driven techniques.
  • To enhance the feasibility of solving large-scale bi-level optimization problems.

Main Methods:

  • The DOMINO framework approximates bi-level problems by sampling the upper-level objective and solving the lower-level problem globally.
  • Integration with a grey-box optimization solver facilitates design of experiments and approximation of MINLP problems.
  • Performance evaluation involved benchmark problems, a land allocation problem, and various data-driven optimization methodologies.

Main Results:

  • The DOMINO framework demonstrates effective approximation and optimization of challenging bi-level MINLP problems.
  • The approach was validated on diverse benchmark instances and a practical Food-Energy-Water Nexus problem.
  • While theoretical global optimality is not guaranteed, algorithmic advancements ensure feasibility for large-scale problems.

Conclusions:

  • The DOMINO framework provides a viable data-driven strategy for addressing complex bi-level optimization problems.
  • The method offers a practical advancement in ensuring feasibility for large-scale MINLP problems.
  • This work contributes to the field of computational optimization by offering a novel framework for tackling intractable problems.