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

Fast Decoupled and DC Powerflow

Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

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...

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...

Design of Transmission Shafts

Design of Transmission Shafts

The design of a transmission shaft is governed by two primary specifications: the power it transmits and its rotational speed. These parameters guide the selection of the shaft's material and cross-sectional dimensions, ensuring that the material's maximum shearing stress remains within the elastic limit while transmitting the desired power at the given speed. The system's power is intrinsically linked to the applied torque. The torque applied to the shaft can be calculated by reconfiguring the...

Design of Transmission Shafts - Stress Analysis

Design of Transmission Shafts - Stress Analysis

Designing a transmission shaft requires a thorough understanding of the stresses induced by bending moments and torques, especially in systems where power is transferred through gears. These forces create force-couple systems at the centers of the shaft's cross-sections, leading to both transverse and torsional loading. Although shearing stresses from transverse loads are typically smaller than those from torques and are often overlooked, the significant normal stresses from these loads...

Unsymmetric Loading of Thin-Walled Members: Problem Solving

Unsymmetric Loading of Thin-Walled Members: Problem Solving

The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...

You might also read

Related Articles

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

Sort by

Same author

Tenascin-C expression contributes to pediatric brainstem glioma tumor phenotype and represents a novel biomarker of disease.

Acta neuropathologica communications·2019

Same author

Why are there so many coexisting species of lizards in Australian deserts?

Oecologia·2017

Same author

Do doctors under-provide, over-provide or do both? Exploring the quality of medical treatment in the Philippines.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2011

Same author

Phase II evaluation of gefitinib in patients with newly diagnosed Grade 4 astrocytoma: Mayo/North Central Cancer Treatment Group Study N0074.

International journal of radiation oncology, biology, physics·2010

Same author

DMD and IL1RAPL1: two large adjacent genes localized within a common fragile site (FRAXC) have reduced expression in cultured brain tumors.

Cytogenetic and genome research·2008

Same author

Non-random inactivation of large common fragile site genes in different cancers.

Cytogenetic and genome research·2007

Search research articles

Related Experiment Video

Updated: Nov 22, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Optimization of decoupling point position using metaheuristic evolutionary algorithms for smart mass customization

C D James¹, Sandeep Mondal²

¹Cypress Semiconductor Technology India Pvt Ltd. (An Infineon Technologies Company), 7th Floor, 65/2, Bagmane Tech Park, Block C, Bagmane Laurel, C V Raman Nagar, Bengaluru, Karnataka 560093 India.

Neural Computing & Applications

|January 11, 2021

Summary

This summary is machine-generated.

This study introduces two evolutionary algorithms to optimize the customer order decoupling point (CODP) for smart mass customization (SMC) in Industry 4.0. These methods enhance modularity and reduce complexity in manufacturing processes for better product customization.

Keywords:

Customer order decoupling point (CODP)Evolutionary algorithm (EA)Learning Optimization Process flow design Smart mass customization (SMC)

More Related Videos

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Related Experiment Videos

Last Updated: Nov 22, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Design and Optimization Strategies of a High-Performance Vented Box

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Area of Science:

Manufacturing Engineering
Operations Research
Artificial Intelligence

Background:

Smart Mass Customization (SMC) in Industry 4.0 aims for autonomous, customer-specific production.
Optimizing the Customer Order Decoupling Point (CODP) is crucial for managing complex product variants and fast-moving manufacturing processes.
Learning and iterative cycles are essential for overcoming complexity barriers in advanced manufacturing.

Purpose of the Study:

To present two metaheuristic evolutionary algorithms for CODP positioning in SMC.
To optimize CODP by enhancing modularity and reducing complexity in manufacturing flow designs.
To provide a framework for process flow design in learning-oriented supply chains.

Main Methods:

Utilized fruit fly optimization and Particle Swarm Optimization (PSO) evolutionary algorithms.
Employed MATLAB programming for iterative searching of optimal process modules.
Developed a complexity reduction model for supply chain and manufacturing flow design.

Main Results:

Demonstrated evolutionary algorithms for effective CODP optimization.
Achieved increased modularity and reduced complexity in manufacturing process flows.
Showcased the application of learning-based PSO iterations for process improvement.

Conclusions:

The proposed algorithms are effective for CODP positioning in SMC.
The methods support process flow design in learning-oriented supply chains with mixed manufacturing.
The complexity reduction model aids in deploying optimized supply chain and manufacturing flows.