Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cable: Problem Solving

Cable: Problem Solving

When dealing with a cable that is fixed to two supports and subjected to uniform loading, it is crucial to determine the maximum tension in the cable. This process can be broken down into several key steps, as outlined below:

Statically Indeterminate Problem Solving

Statically Indeterminate Problem Solving

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...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Reinforcement Schedules

Reinforcement Schedules

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,...

Trial and Error and Algorithm

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...

You might also read

Related Articles

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

Sort by

Same author

Measuring Consumer Willingness to Pay to Reduce Health Risks of Contracting Dengue Fever.

International journal of environmental research and public health·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

A branch and bound algorithm for optimal television commercial scheduling.

¹Department of Intelligent Production Engineering, National Taichung University of Science and Technology, Taichung 40401, Taiwan.

Mathematical Biosciences and Engineering : MBE

|April 18, 2022

Summary

This study introduces a new method for scheduling TV commercials to maximize revenue while minimizing lateness. The developed algorithm efficiently optimizes advertising placement on television channels.

Keywords:

LFJ/EDD rule TV commercials branch and bound algorithm scheduling service level requirement

More Related Videos

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Published on: May 29, 2020

Barnes Maze Testing Strategies with Small and Large Rodent Models

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Related Experiment Videos

Last Updated: Sep 26, 2025

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Published on: May 29, 2020

Barnes Maze Testing Strategies with Small and Large Rodent Models

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Area of Science:

Operations Research
Media Management

Background:

Television advertising is a primary revenue source for broadcasters.
Effective commercial scheduling is crucial for maximizing revenue and minimizing penalties.
Existing literature lacks focus on TV commercial scheduling with availability constraints.

Purpose of the Study:

To address the TV commercial scheduling problem with service-level requirements.
To minimize the maximum lateness of commercials on TV channels.
To adapt machine scheduling problem solutions for the TV advertising context.

Main Methods:

Developed an exact branch and bound algorithm.
Utilized least flexible job first (LFJ) and earliest due date first (EDD) rules.
Incorporated network flow methods for availability constraints.

Main Results:

The proposed bounding scheme is highly effective.
The branch and bound algorithm generates a very low percentage of nodes.
The algorithm successfully obtains optimal solutions for TV commercial scheduling.

Conclusions:

The new algorithm provides an effective solution for TV commercial scheduling.
This approach optimizes revenue and minimizes lateness for TV stations.
The study bridges a gap in the literature by considering availability constraints.