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

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...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

Mechanical Efficiency of Real Machines

Mechanical Efficiency of Real Machines

The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...

Multimachine Stability

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Modeling and Similitude

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

You might also read

Related Articles

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

Sort by

Same author

Allocation of Strategic Positions for Storage of Meat Products Requiring Cold Chain.

Foods (Basel, Switzerland)·2025

Same author

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.

Sensors (Basel, Switzerland)·2024

Same author

Systematic Configurator for Complexity Management in Manufacturing Systems.

Entropy (Basel, Switzerland)·2024

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 31, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Measuring Complexity in Manufacturing: Integrating Entropic Methods, Programming and Simulation.

Germán Herrera-Vidal¹, Jairo R Coronado-Hernández², Ivan Derpich-Contreras³

¹Industrial Engineering School, Universidad del Sinú, Cartagena 130001, Colombia.

Entropy (Basel, Switzerland)

|January 24, 2025

Summary

This summary is machine-generated.

This study introduces an entropic complexity metric for manufacturing systems. Optimizing production scheduling using this metric can reduce bottlenecks and enhance industrial efficiency.

Keywords:

complexity entropic manufacturing systems measurement methodology

More Related Videos

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

Related Experiment Videos

Last Updated: May 31, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

Area of Science:

Industrial Engineering
Operations Research
Information Theory

Background:

Manufacturing systems face challenges with complexity, impacting efficiency.
Quantifying complexity is crucial for effective production improvement strategies.
Existing methods may not fully capture dynamic aspects of manufacturing complexity.

Purpose of the Study:

To develop and validate a methodology for measuring entropic complexity in production environments.
To establish an integral entropic metric for assessing manufacturing system complexity.
To provide a quantitative framework for managing production complexity.

Main Methods:

Utilized discrete event simulation and programming techniques.
Applied Shannon's information theory to measure entropic complexity.
Conducted statistical analysis, including ANOVA, for validation.

Main Results:

Production sequence and product volume significantly influence workstation complexity.
Station A exhibited lower complexity (0.4154–0.9913 bits) than stations B and C (up to 2.2084 bits).
Optimizing production scheduling demonstrated potential to reduce bottlenecks and improve system efficiency.

Conclusions:

The developed methodology offers a quantitative approach to measure static and dynamic complexity.
The entropic metric provides a practical tool for anticipating and managing complexity in manufacturing.
This research enhances efficiency and competitiveness in the industrial sector through complexity management.