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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

821
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...
821
Reinforcement Schedules01:24

Reinforcement Schedules

268
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,...
268
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

516
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
516
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

123
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...
123
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

537
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...
537
Machines: Problem Solving I01:22

Machines: Problem Solving I

471
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...
471

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Related Experiment Video

Updated: Oct 18, 2025

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

Published on: October 14, 2017

11.9K

Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling.

Yunmei Yuan1, Hongyu Li1, Lili Ji1

  • 1School of Nanjing Institute of Railway Technology, Nanjing, Jiangsu 210031, China.

Computational Intelligence and Neuroscience
|October 5, 2021
PubMed
Summary

This study introduces a novel deep reinforcement learning strategy for uncertain logistics vehicle path planning. The method significantly reduces driving distance by 60.71% compared to traditional approaches.

Related Experiment Videos

Last Updated: Oct 18, 2025

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

Published on: October 14, 2017

11.9K

Area of Science:

  • Logistics and Transportation Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Online vehicle path planning in logistics faces complexity due to transportation system uncertainties, particularly in last-mile delivery.
  • Existing heuristic algorithms require extensive constraints and computation time, limiting their applicability with new technologies like machine learning.

Purpose of the Study:

  • To develop an optimization strategy for uncertain logistics transportation path planning that minimizes travel time.
  • To address the limitations of traditional methods by leveraging deep reinforcement learning for complex routing problems.

Main Methods:

  • Proposed a deep reinforcement learning strategy to convert uncertain logistics routing problems into vehicle path planning problems.
  • Designed an embedded pointer network for optimal solution acquisition.
  • Employed an unsupervised method for offline parameter training to avoid high delays and reduce computation time.

Main Results:

  • The proposed strategy effectively solves uncertain logistics scheduling problems within limited computing time.
  • Demonstrated significant improvement over existing strategies, reducing driving distance by 60.71% compared to traditional mathematical procedures.
  • Analyzed the impact of key parameters on the strategy's effectiveness.

Conclusions:

  • The deep reinforcement learning-based approach offers a superior solution for uncertain logistics vehicle path planning.
  • The unsupervised training method makes the strategy practical for real-time applications with high demand density.
  • The strategy provides substantial reductions in operational costs through minimized driving distances.