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Bi-level Data-driven Enterprise-wide Optimization with Mixed-integer Nonlinear Scheduling Problems.

Hasan Nikkhah1,2, Zahir Aghayev1,2, Amir Shahbazi1,2

  • 1Department of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, 06269, CT,USA.

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Summary
This summary is machine-generated.

This study introduces the DOMINO framework for integrated enterprise-wide optimization (EWO), effectively solving complex planning and scheduling problems. DOMINO-NOMAD demonstrated superior performance in optimizing production and meeting market demands.

Keywords:
Bi-level programmingData-driven optimizationIntegrated planning and schedulingMixed-integer nonlinear programming

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Area of Science:

  • Operations Research
  • Process Systems Engineering
  • Optimization

Background:

  • Enterprise-wide optimization (EWO) requires holistic decision-making for efficient resource utilization in process industries.
  • Sequential planning and scheduling often yield impractical solutions due to their interdependence.
  • Bi-level programming offers integrated solutions but faces challenges with mixed-integer nonlinear programming (MINLP) formulations.

Purpose of the Study:

  • To develop and apply a data-driven algorithm, DOMINO, for solving single-leader multi-follower planning and scheduling problems.
  • To address the limitations of existing methods in handling complex, interdependent optimization layers.
  • To evaluate the performance of DOMINO with different optimization solvers on large-scale industrial problems.

Main Methods:

  • Utilized the Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework.
  • Applied DOMINO to a multi-product methyl methacrylate polymerization process (Traveling Salesman Problem formulation).
  • Extended DOMINO to a high-dimensional nonlinear crude oil refinery operation problem, comparing NOMAD and ARGONAUT optimizers.

Main Results:

  • DOMINO successfully achieved near-optimal guaranteed feasible solutions for planning and scheduling.
  • The DOMINO-NOMAD combination consistently outperformed DOMINO-ARGONAUT in solution quality and feasibility.
  • The study demonstrated DOMINO's capability in optimizing production targets and meeting market demands for complex EWO.

Conclusions:

  • The DOMINO framework provides an effective approach for integrated bi-level optimization in process industries.
  • DOMINO enables efficient resource allocation and improved decision-making for large-scale EWO problems.
  • The choice of optimization solver (NOMAD vs. ARGONAUT) significantly impacts performance within the DOMINO framework.