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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Upstream Processing01:27

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...

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相关实验视频

Updated: Jun 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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在一个真正的工业4.0环境中,生产计划与多机器人任务分配.

Zohreh Shakeri1, Khaled Benfriha2, Mohsen Varmazyar3

  • 1Laboratoire Conception de Produits et Innovation (LCPI), Arts et Metiers Institute of Technology, 75013, Paris, France. zohreh.shakeri@ensam.eu.

Scientific reports
|January 13, 2025
PubMed
概括

这项研究优化了多机器人灵活工作室为工业4.0系统的调度. 一种新的拟议基因算法 (PGA) 显著提高了复杂制造调度的基本方法的效率.

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相关实验视频

Last Updated: Jun 7, 2026

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Published on: October 14, 2017

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科学领域:

  • 运营研究 运营研究
  • 工业工程 工业工程 工业工程
  • 制造系统制造系统的制造

背景情况:

  • 工业4.0要求高效的生产系统,使机器人调度至关重要.
  • 现有的研究往往简化了机器人工作室的问题,忽视了机器人的多样性和资源配置.
  • 智能制造需要精确的调度材料传输机器人,特别是有限的缓冲区和阻塞条件.

研究的目的:

  • 为了解决复杂的多机器人灵活工作车间 (MRFJS) 计划问题,使用有限的缓冲器.
  • 开发和评估一种新的遗传算法 (GA),以在现实的工业4.0场景中优化生产时间表.
  • 为了最大限度地减少在一个系统与非相同的并行机器和多样化的机器人 makespan.

主要方法:

  • 制定一个混合整数编程 (MILP) 模型,以尽量减少 makespan.
  • 开发一种新的基因算法 (GA),结合罗伊和苏斯曼的替代图.
  • 使用各种尺度和真实制造厂数据进行计算测试.

主要成果:

  • 拟议的遗传算法 (PGA) 实现了0.25%的平均相对偏差 (ARD).
  • PGA表现出比基本遗传算法 (BGA) 的34%的改善,基本遗传算法的ARD为0.38%.
  • 该算法在解决现实世界的生产环境中复杂的调度问题方面被证明是有效的.

结论:

  • 开发的PGA对于优化工业4.0环境中的MRFJS调度非常有效.
  • 该研究强调了考虑机器人多样性和缓冲区限制对于现实的调度的重要性.
  • 提出的方法为智能制造效率和复杂的调度解决方案提供了显著的进步.