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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

243
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
243
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

150
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
150
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

502
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
502
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

103
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
103
Reinforcement01:23

Reinforcement

353
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
353
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

743
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...
743

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相关实验视频

Updated: Sep 17, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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一个多目标的协作强化学习算法,用于灵活的工作车间安排.

Jian Li1, Shifa Li2, Pengbo He2

  • 1School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471000, China. li_jian@haust.edu.cn.

Scientific reports
|July 2, 2025
PubMed
概括

这项研究引入了一种新的多目标强化学习算法,用于灵活的工作车间安排. 拟议的方法通过优化产量和能源消耗来提高调度效率,优于现有的算法.

关键词:
合作的代理人强化学习学习.灵活的工作车间调度问题马尔科夫决策过程

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科学领域:

  • 运营研究 运营研究
  • 人工智能的人工智能
  • 制造系统工程 制造系统工程

背景情况:

  • 灵活的工作车间安排是复杂的,具有竞争的目标,如尽量减少制造商和能源消耗.
  • 现有的算法往往难以有效地平衡这些多重目标.
  • 基于智能代理的方法为动态和高效的调度提供了潜力.

研究的目的:

  • 开发一个多目标的协作智能代理强化学习算法,用于灵活的工作车间安排.
  • 为了同时优化makepan和总能耗.
  • 在现实场景中提高总体调度效率和实用性.

主要方法:

  • 建立了一个灵活的工作车间调度优化的数学模型,将makepan和能源消耗作为目标.
  • 一个断层图被用来表示智能代理的状态特征.
  • 两个带有编码器解码器组件的智能代理被设计用于同时执行任务和机器决策.
  • 使用时间差奖励构建了一个多目标的马尔科夫决策过程培训模型.

主要成果:

  • 拟议的算法在标准实例上表现出高于现有方法的性能.
  • 评估指标包括超量,集覆盖范围和反转的代际距离证实了算法的有效性.
  • 一个现实世界的案例研究验证了开发的方法的实际适用性和效率.

结论:

  • 多目标协作智能代理强化学习算法显著改善了灵活的工作车间安排.
  • 该方法有效地平衡了产量和能源消耗,为制造提供了实际的解决方案.
  • 这种方法代表了复杂生产环境的智能调度的实质性进步.