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This study enhances supply chain management by integrating planning and scheduling using the DOMINO framework. It offers a novel approach for complex, multi-level optimization problems, improving efficiency and performance.

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Optimization

Background:

  • Traditional supply chain management uses sequential decision-making, often neglecting interdependencies between planning and scheduling.
  • Multi-level programming offers a holistic approach but faces computational challenges due to NP-hard complexity.

Purpose of the Study:

  • To extend the DOMINO framework for integrated supply chain planning and scheduling with multiple lower-level problems.
  • To address the lack of attention in literature for such complex, multi-follower optimization problems.

Main Methods:

  • Utilized a data-driven optimization algorithm (DOMINO) to solve integrated planning and scheduling problems.
  • Developed a method involving sampling production targets and solving deterministic scheduling problems per sample.
  • Employed a data-driven optimizer to find feasible, near-optimal solutions for the integrated problem.

Main Results:

  • Successfully adapted the DOMINO framework to handle multi-follower, integrated planning and scheduling.
  • Demonstrated the approach's applicability through a two-product planning and scheduling case study.
  • Achieved feasible, near-optimal solutions for complex supply chain optimization.

Conclusions:

  • The enhanced DOMINO framework provides a robust solution for integrated supply chain planning and scheduling.
  • This approach effectively manages the hierarchy and interconnectivity in complex supply chain systems.
  • The method offers a promising direction for tackling advanced, multi-level optimization challenges in operations research.