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Flexible Networked Machine Integrated Scheduling Algorithm Based on the Dynamic Root Node Operation Set Considering Reverse Scheduling

Electronics (Switzerland), ISSN: 2079-9292, Vol: 12, Issue: 3
2023
  • 2
    Citations
  • 0
    Usage
  • 4
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
    • Citation Indexes
      2
  • Captures
    4
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

New Electronics Study Results Reported from Harbin University of Science and Technology (Flexible Networked Machine Integrated Scheduling Algorithm Based on the Dynamic Root Node Operation Set Considering Reverse Scheduling)

2023 FEB 28 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Life Science Daily -- Investigators publish new report on electronics. According to

Article Description

Aiming at the problem that the previous flexible machine network integrated scheduling algorithm only considers positive sequence scheduling, which leads to the extension of product completion time, a reverse-order machine network integrated scheduling algorithm based on the dynamic root node operation set is proposed. In order to avoid the constraints of multi-predecessor operations in the process of forward-order scheduling, an encoding method based on dynamic root node operation set is proposed to ensure the validity of constraints among operations. The crossover methods based on crossover row vector and subtree are proposed to ensure the legitimacy of offspring individuals. The chaotic mutation method based on sibling operation and the random mutation method based on mutation row vector are proposed, respectively, to ensure the diversity of the population. A local search strategy based on the critical operation machine set is proposed, which enhances the search ability of the algorithm for optimal solutions. The comparative experimental results show that the proposed algorithm’s solving speed and solution quality outperform other comparison algorithms.

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