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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

367
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
367
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

226
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...
226
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

337
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures enhance...
337
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

152
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
152
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

182
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
182
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

980
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
980

You might also read

Related Articles

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

Sort by
Same author

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026
Same author

Development of a scientific version TPS workflow for BNCT dosimetry based on DICOM and Monte Carlo engine.

Physical and engineering sciences in medicine·2026
Same author

Advancements and insights into newborn screening with tandem mass spectrometry in China: a comprehensive descriptive analysis (2017-2021).

BMJ paediatrics open·2026
Same author

Microbial biodegradation of polyethylene in estuarine sediments: metabolic pathways of Pseudomonas under denitrifying conditions.

Journal of hazardous materials·2026
Same author

Artificial gauge fields for sculpting topological modes on photonic chips.

Nature communications·2026
Same author

Synergistic Effect of a Combined 10-MDP/γ-MPTS Primer on Glass-Ceramic Bonding: A Multimodal Study.

International dental journal·2026

Related Experiment Video

Updated: Dec 22, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.2K

Local convex hulls for a special class of integer multicommodity flow problems.

Zhiyuan Lin1, Raymond S K Kwan1

  • 1School of Computing, University of Leeds, Leeds, LS2 9JT UK.

Computational Optimization and Applications
|May 2, 2020
PubMed
Summary

This study generalizes a local convex hull method for integer multicommodity flow problems, introducing a "2-facet QuickHull" algorithm. While effective for rolling stock scheduling, computational limits arise with many commodity types or data points in high dimensions.

Keywords:
Convex hull computationInteger multicommodity network flowRolling stock scheduling

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.8K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.3K

Related Experiment Videos

Last Updated: Dec 22, 2025

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus
05:57

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

7.2K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.8K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.3K

Area of Science:

  • Operations Research
  • Combinatorial Optimization
  • Transportation Science

Background:

  • Rolling stock scheduling is a complex problem within integer multicommodity flow.
  • Existing methods for convex hull computation have limitations in high-dimensional spaces.
  • Previous research established foundational work in rolling stock scheduling problems.

Purpose of the Study:

  • To generalize a local convex hull method for integer multicommodity flow problems.
  • To analyze the feasibility and performance of this method in high-dimensional cases.
  • To develop and evaluate a modified QuickHull algorithm ('2-facet QuickHull').

Main Methods:

  • Theoretical analysis of local convex hull properties, including facet analysis.
  • Development of the '2-facet QuickHull' algorithm, a modification of the standard QuickHull.
  • Empirical experimentation using problem instances from ScotRail and Southern Railway, alongside artificial instances.

Main Results:

  • Theoretically, the main hull of a local convex hull has at most two nonzero facets under specific conditions.
  • Standard QuickHull efficiently computes convex hulls for typical rolling stock scheduling problems.
  • The '2-facet QuickHull' shows advantages for artificial instances with numerous compatible commodities.
  • Both methods face computational challenges with a high number of commodity types or data points in high dimensions.

Conclusions:

  • The generalized local convex hull method and '2-facet QuickHull' offer potential improvements for specific integer multicommodity flow problems.
  • The standard QuickHull remains effective for practical rolling stock scheduling scenarios.
  • Computational limitations exist for extremely complex instances, indicating areas for future research.