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Bilevel optimization of mixed-integer nonlinear integrated planning and scheduling problems using the DOMINO

Hasan Nikkhah1,2, Vassilis M Charitopoulos3, Styliani Avraamidou4

  • 1Department of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, CT, 06260, USA.

ESCAPE. European Symposium on Computer Aided Process Engineering
|February 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven optimization strategy to solve complex supply chain planning and scheduling problems. The DOMINO framework effectively handles integrated bilevel programming, yielding feasible production schedules.

Keywords:
Data-driven optimizationbilevel programmingenterprise-wide optimizationmixed-integer nonlinear programmingproduction planningscheduling

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Data-Driven Optimization

Background:

  • Integrated planning and scheduling in multi-level supply chains are complex due to interdependence and market volatility.
  • Sequential approaches often lead to infeasible production schedules.

Purpose of the Study:

  • To develop a data-driven optimization approach for integrated planning and scheduling problems formulated as bilevel programming.
  • To address challenges in multi-level supply chain decision-making.

Main Methods:

  • Formulation of enterprise-wide decision-making as a bilevel optimization problem with linear production planning and mixed-integer nonlinear scheduling.
  • Application of the DOMINO framework, a data-driven optimization strategy for bilevel problems.

Main Results:

  • Demonstrated effectiveness on diverse case studies, including crude oil scheduling and continuous manufacturing processes.
  • DOMINO successfully handles high-dimensional, mixed-integer nonlinear lower levels in bilevel programming.

Conclusions:

  • The DOMINO framework provides a robust solution for integrated enterprise-wide optimization problems.
  • This approach ensures feasible production schedules across complex supply chain networks.