skip to main content
research-article

Many-Objective Genetic Programming for Job-Shop Scheduling

Published:13 February 2023Publication History
Skip Abstract Section

Abstract

This article overviews a recent PhD dissertation representing the first effort at many-objective optimization in job shop scheduling (JSS). The thesis develops genetic programming hyperheuristic (GP-HH) approaches to evolve effective dispatching rules for many conflicting objectives in JSS problems. The aim is to develop GP-HH methods that alleviate issues related to many-objective optimization in JSS problems and evolve new effective dispatching rules capable of enhancing job shops' productivity.

References

  1. Branke, J., Nguyen, S., Pickardt, C., Zhang, M.: Automated Design of Production Scheduling Heuristics: A Review. IEEE Trans. Evolutionary Computation 20(1), 110--124 (2016)Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18(4), 577--601 (2014)Google ScholarGoogle ScholarCross RefCross Ref
  3. Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: A genetic programming approach. In: Proceedings of Genetic and Evolutionary Computation Conference. pp. 257--264. ACM (2010)Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 28(3), 392--403 (1998)Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Jones, A., Rabelo, L.C., Sharawi, A.T.: Survey of job shop scheduling techniques. Wiley encyclopedia of electrical and electronics engineering (2001)Google ScholarGoogle Scholar
  6. Leung, J.Y.T. (ed.): Handbook of Scheduling - Algorithms, Models, and Performance Analysis. Chapman and Hall/CRC (2004)Google ScholarGoogle Scholar
  7. Pinedo, M.L.: Scheduling: theory, algorithms, and systems. Springer Science & Business Media (2012)Google ScholarGoogle ScholarCross RefCross Ref
  8. Tsai, C.W., Rodrigues, J.J.P.C.: Metaheuristic Scheduling for Cloud: A Survey. IEEE Systems Journal 8(1), 279--291 (2014)Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGEVOlution
    ACM SIGEVOlution  Volume 15, Issue 4
    December 2022
    17 pages
    EISSN:1931-8499
    DOI:10.1145/3584367
    Issue’s Table of Contents

    Copyright © 2023 Copyright is held by the owner/author(s)

    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 13 February 2023

    Check for updates

    Qualifiers

    • research-article
  • Article Metrics

    • Downloads (Last 12 months)35
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader