Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

144
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,...
144
Machines: Problem Solving II01:30

Machines: Problem Solving II

308
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
308
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

377
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...
377
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Machines: Problem Solving I01:22

Machines: Problem Solving I

320
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
320
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
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...
53

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Risk factors on the unintentional injuries among rural children aged 0-12 in Shaanxi province].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2013
Same author

Adcyap1r1 genotype, posttraumatic stress disorder, and depression among women exposed to childhood maltreatment.

Depression and anxiety·2013
Same author

Current status and challenge of Human Parasitology teaching in China.

Pathogens and global health·2012
Same author

Molecular characterization of heterogeneous mesenchymal stem cells with single-cell transcriptomes.

Biotechnology advances·2012
Same author

Surgical treatment of ossification of the ligamentum flavum associated with dural ossification in the thoracic spine.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2012
Same author

Broadband focusing ultrasonic transducers based on dimpled LiNbO3 plate with inversion layer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2012
Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026
Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026
Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026
Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026
Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

529

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling

Xiao Wang1, Peisi Zhong2, Mei Liu3

  • 1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific Reports
|April 19, 2024
PubMed
Summary
This summary is machine-generated.

A new deep temporal difference reinforcement learning algorithm effectively solves the flexible double shop scheduling problem (FDSSP), minimizing production time. This approach improves manufacturing efficiency by optimizing task scheduling in complex production environments.

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.4K

Related Experiment Videos

Last Updated: Jun 28, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

529
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.4K

Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Manufacturing Engineering

Background:

  • The flexible double shop scheduling problem (FDSSP) integrates job shop and assembly shop complexities.
  • Scheduling associations between related tasks present significant challenges in manufacturing.

Purpose of the Study:

  • To develop an effective algorithm for minimizing makespan in the FDSSP.
  • To address the intricate task scheduling associations within flexible manufacturing systems.

Main Methods:

  • Formulated FDSSP as a mathematical model incorporating assembly constraints.
  • Translated the problem into a Markov decision process for direct strategy selection.
  • Utilized a deep neural network with ten state features and eight constructive heuristics for decision-making.
  • Implemented a deep temporal difference reinforcement learning framework.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to existing methods.
  • Effectively minimized the makespan for the flexible double shop scheduling problem.
  • Validated through extensive comparative experiments.

Conclusions:

  • The developed deep temporal difference reinforcement learning algorithm offers a robust solution for FDSSP.
  • This approach has practical implications for improving efficiency in the manufacturing industry.
  • Successfully addresses complex scheduling associations in flexible manufacturing.