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Order scheduling optimization in manufacturing enterprises based on MDP and dynamic programming.

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This study introduces a Markov decision process model for optimizing manufacturing order scheduling, outperforming traditional methods. The proposed strategy aims to maximize revenue in Industry 4.0 production systems.

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Area of Science:

  • Operations Research
  • Manufacturing Systems Engineering
  • Industrial Engineering

Background:

  • Order scheduling is critical for manufacturing efficiency in Industry 4.0.
  • Traditional scheduling methods like first-come, first-served may not maximize revenue.
  • Dynamic production environments require adaptive scheduling strategies.

Purpose of the Study:

  • To develop an optimal order scheduling strategy for manufacturing enterprises.
  • To maximize revenue in manufacturing production systems using a novel model.
  • To enhance production efficiency in the context of Industry 4.0.

Main Methods:

  • A finite horizon Markov decision process model was developed.
  • The model considers two equipment sets and three order types with varying lead times.
  • Dynamic programming was integrated for optimal strategy determination, simulated using Python.

Main Results:

  • The proposed Markov decision process model significantly improves upon the first-come, first-served approach.
  • Experimental cases validated the model's superiority in maximizing revenue.
  • Sensitivity analysis confirmed the strategy's applicability concerning device service hours and order completion rates.

Conclusions:

  • The developed order scheduling strategy offers a superior alternative for manufacturing enterprises.
  • The model provides a robust framework for revenue maximization in dynamic production settings.
  • This research contributes to optimizing manufacturing operations within Industry 4.0.