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Data-Driven Optimization of Mixed-integer Bi-level Multi-follower Integrated Planning and Scheduling Problems Under

Burcu Beykal1,2, Styliani Avraamidou3, Efstratios N Pistikopoulos4,5

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

Computers & Chemical Engineering
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Summary
This summary is machine-generated.

This study integrates supply chain planning and scheduling under uncertainty using advanced optimization techniques. It presents a novel data-driven framework (DOMINO) to achieve guaranteed feasible solutions for complex industrial systems.

Keywords:
Bi-level ProgrammingData-driven OptimizationDemand UncertaintyFeasibilityIntegrated Planning and SchedulingStochastic Analysis

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Supply Chain Management

Background:

  • Coordinating supply chain layers is crucial for industrial processes and decision-making.
  • Modeling and optimizing interdependent supply chain systems remain challenging.
  • Demand uncertainty complicates medium-term planning and short-term scheduling.

Purpose of the Study:

  • To simultaneously model and optimize medium-term planning and short-term scheduling under demand uncertainty.
  • To develop a data-driven optimization approach for complex supply chain problems.
  • To extend the DOMINO framework for multi-follower stochastic formulations.

Main Methods:

  • Utilized mixed-integer bi-level multi-follower programming.
  • Employed scenario analysis and data-driven optimization.
  • Extended the DOMINO framework to handle stochastic, multi-follower problems.

Main Results:

  • Presented guaranteed feasible solutions for integrated planning and scheduling problems.
  • Addressed both linear and nonlinear mixed-integer bi-level formulations.
  • Characterized the impact of scheduling complexity on solution performance.

Conclusions:

  • The data-driven approach provides guaranteed feasible solutions for simultaneous planning and scheduling.
  • The extended DOMINO framework effectively handles complex, hierarchical supply chain optimization.
  • This methodology offers a robust solution for industrial processes facing demand uncertainty.