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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Published on: October 14, 2017

Genetic programming for evolving due-date assignment models in job shop environments.

Su Nguyen1, Mengjie Zhang, Mark Johnston

  • 1Evolutionary Computation Research Group, Victoria University of Wellington, Wellington, New Zealand su.nguyen@ecs.vuw.ac.nz.

Evolutionary Computation
|April 26, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces two genetic programming (GP) methods to evolve better due-date assignment models (DDAMs) for job shops. The evolved models provide more accurate delivery performance estimates than existing dynamic DDAMs.

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11:53

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Published on: October 14, 2017

Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Systems Engineering

Background:

  • Due-date assignment is critical for job shop scheduling and delivery performance.
  • The inherent stochastic and dynamic nature of job shops complicates the development of effective due-date assignment models (DDAMs).

Purpose of the Study:

  • To develop novel methods for evolving advanced DDAMs tailored for dynamic job shop environments.
  • To enhance the accuracy and reusability of due-date estimation in job shop scheduling.

Main Methods:

  • Utilized two genetic programming (GP) approaches to automatically evolve DDAMs.
  • Compared the performance of evolved DDAMs against existing dynamic DDAMs.
  • Investigated the impact of using operation-based information versus aggregate job and machine information.

Main Results:

  • The evolved DDAMs demonstrated superior accuracy in estimating delivery performance compared to existing dynamic models.
  • Evolved DDAMs exhibited promising reusability across different job shop scenarios.
  • Operation-based evolved DDAMs outperformed those using aggregate job and machine data.

Conclusions:

  • Genetic programming offers a powerful approach for evolving effective DDAMs in complex job shop settings.
  • Operation-level information is more beneficial for DDAM evolution than aggregate data.
  • The proposed GP-evolved DDAMs represent a significant advancement for scheduling system performance.