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 Experiment Videos

Production scheduling and rescheduling with genetic algorithms.

C Bierwirth1, D C Mattfeld

  • 1Department of Economics, University of Bremen, Box 330440, D-28334 Bremen, Germany. chris@logistik.uni-bremen.de

Evolutionary Computation
|April 13, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

[Erratum to: Imaging of chronic inflammatory bowel diseases in childhood and adolescence. Repetitorium].

Der Radiologe·2020
Same author

[Imaging of chronic inflammatory bowel diseases in childhood and adolescence : Repetitorium].

Der Radiologe·2020
Same author

Road Traffic Related Injury Research and Informatics. New Opportunities for Biomedical and Health Informatics as a Contribution to the United Nations' Sustainable Development Goals?

Methods of information in medicine·2015
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
See all related articles

A novel Genetic Algorithm offers superior job shop scheduling solutions for dynamic and non-deterministic environments. This advanced technique significantly enhances schedule quality and outperforms traditional production control methods.

Area of Science:

  • Operations Research
  • Computer Science
  • Industrial Engineering

Background:

  • Job shop scheduling is a complex combinatorial optimization problem.
  • Existing methods struggle with dynamic and non-deterministic production environments.
  • Need for efficient algorithms to improve production control.

Purpose of the Study:

  • To present a general model for job shop scheduling applicable to various environments.
  • To introduce and evaluate a Genetic Algorithm for solving the job shop scheduling problem.
  • To demonstrate the algorithm's effectiveness in dynamic and non-deterministic settings.

Main Methods:

  • Developed a general job shop scheduling model.
  • Implemented a Genetic Algorithm with an efficient decoding procedure.

Related Experiment Videos

  • Tested the algorithm in dynamic and non-deterministic production environments under varying workloads.

    • Main Results:

    • The Genetic Algorithm significantly improved schedule quality.
    • The proposed decoding procedure enhanced efficiency.
    • The technique outperformed conventional production control methods in experiments.
    • Achieved superior results at reasonable computational costs.

    Conclusions:

    • The presented Genetic Algorithm is highly effective for job shop scheduling.
    • The method provides a robust solution for dynamic and non-deterministic environments.
    • This approach offers a significant advancement over traditional production control strategies.