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

Optimization Problems01:26

Optimization Problems

220
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
220
Methods of Medium Optimization01:28

Methods of Medium Optimization

70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

321
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
321
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
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...
438
Heuristics01:21

Heuristics

716
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
716
Systems of Linear Equations in Two Variables01:25

Systems of Linear Equations in Two Variables

447
Solving a system of linear equations is a fundamental concept in algebra. A system of equations consists of two or more linear equations involving the same set of variables. One of the most efficient algebraic methods for solving such systems is the substitution method. This technique involves expressing one variable in terms of the other from one equation and substituting it into the second equation. This method is particularly useful when one of the equations is easily rearranged.Consider the...
447

You might also read

Related Articles

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

Sort by
Same author

Microplastic contamination across different feeding guilds of commercially important fishes from the Southeast Coast of India.

Environmental monitoring and assessment·2026
Same author

"Spiro-pyrrolizidine-benzyloxy hybrid as synergistic partner to doxorubicin cardio-safe breast cancer chemotherapy".

Future medicinal chemistry·2025
Same author

2: 1 AV block post ASD device closure - What is the mechanism.

Indian pacing and electrophysiology journal·2025
Same author

The Results of ADVANCE-CIDP IVIG Trial: Intravenous Immunoglobulin 10% Therapy With GAMMAGARD LIQUID/Kiovig for Treatment of Relapse in Chronic Inflammatory Demyelinating Polyradiculoneuropathy.

European journal of neurology·2025
Same author

A review-chitosan nanoparticles towards enhancing nutrient use efficiency in crops.

International journal of biological macromolecules·2025
Same author

Comparative <i>In silico</i> and <i>In vitro</i> Studies of Novel Zinc/Tin Metal Coordinates Bearing BRCA-1 Mimetics on WTp53 and MTp53 Proteins.

Protein and peptide letters·2025

Related Experiment Video

Updated: Apr 30, 2026

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

12.6K

Heuristics for multiobjective optimization of two-sided assembly line systems.

N Jawahar1, S G Ponnambalam2, K Sivakumar1

  • 1Department of Mechanical Engineering, Thiagarajar College of Engineering, Madurai, Tamilnadu 625 015, India.

Thescientificworldjournal
|May 3, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces two heuristics, Enumerative Heuristic Algorithm (EHA) and Simulated Annealing Algorithm (SAA), for optimizing two-sided assembly line balancing (TALBP). These methods aim to minimize workstations and unbalance time, enhancing manufacturing productivity.

More Related Videos

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

673

Related Experiment Videos

Last Updated: Apr 30, 2026

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

12.6K
Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

673

Area of Science:

  • Operations Research
  • Industrial Engineering
  • Manufacturing Systems

Background:

  • Two-sided assembly lines are common in manufacturing for products like vehicles and heavy machinery.
  • Assembly line balancing (ALB) is crucial for optimizing flow line manufacturing performance and productivity.
  • Balancing two-sided assembly lines presents unique challenges due to task assignment flexibility.

Purpose of the Study:

  • To address the task assignment and balancing problem for two-sided assembly lines (TALBP).
  • To simultaneously minimize the number of workstations and the unbalance time among workstations.
  • To develop efficient heuristic algorithms for solving the multi-objective TALBP.

Main Methods:

  • Proposed two heuristic approaches: Enumerative Heuristic Algorithm (EHA) for small/medium problems and Simulated Annealing Algorithm (SAA) for large problems.
  • Focused on evolving an optimal Pareto front for the TALBP.
  • Considered task assignments to the L side, R side, or either side (E).

Main Results:

  • Demonstrated the effectiveness of EHA and SAA through illustrative example problems.
  • Compared the performance of the proposed heuristics against existing test problems.
  • Provided a method for finding Pareto optimal solutions for TALBP.

Conclusions:

  • The proposed EHA and SAA heuristics offer effective solutions for the multi-objective two-sided assembly line balancing problem.
  • These algorithms contribute to improving efficiency and productivity in flow line manufacturing systems.
  • The study validates the heuristics' performance on various test instances, supporting their practical application.