Skip to main content
SpringerLink
Log in

Comparison of Multiple Objective Genetic Algorithms for Parallel Machine Scheduling Problems

  • Conference paper
  • First Online:
Book cover Evolutionary Multi-Criterion Optimization (EMO 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Included in the following conference series:

Abstract

Many multiple objective genetic algorithms have been developed to approximate the efficient frontier of solutions for multiple objective optimization problems. However, only a limited number of comparison studies have been performed on practical problems. One of the reasons for this may be the lack of commonly accepted measures to compare the solution quality of sets of approximately optimal solutions. In this paper, we perform an extensive set of experiments to quantitatively compare the solutions of two competing algorithms for a bi-criteria parallel machine-scheduling problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aksoy, Y., T. W. Butler and E. D. Minor, (1996) Comparative studies in interactive multiple objective mathematical programming, European Journal of Operational Research, 89, 408–422.

    Article  MATH  Google Scholar 

  2. Cochran, J. K., S.-M. Horng, and Fowler, J. W., (2000), A Multi Population Genetic Algorithm to Solve Parallel Machine Scheduling Problem with Sequence Dependent Setups. Working Paper, Arizona State University, Department of Industrial Engineering.

    Google Scholar 

  3. Coello, C. A. C., (Aug., 1999), A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques, An International Journal of Knowledge and Information Systems, 1(3):269–308.

    Google Scholar 

  4. Czyzak, P. and A. Jaszkiewicz, (1998). Pareto Simulated annealing — A Metaheuristic Technique for Multiple-Objective Combinatorial Optimization. Journal of Multi-Criteria Decision Analyses, 7, 34–47.

    Article  MATH  Google Scholar 

  5. Daniels, R. L., (1992). Analytical evaluation of multi-criteria heuristics. Management Science, 38(4), 501–513.

    MATH  Google Scholar 

  6. De, P., J. B. Ghosh, and C. E. Wells, (1992), Heuristic Estimation of the Efficient Frontier for a Bi-Criteria Scheduling Problem. Decision Sciences, 23, 596–609.

    Article  Google Scholar 

  7. Kim B., E. S. Gel, M. W. Carlyle, J. W. Fowler, (2000), A new technique to compare algorithms for bi-criteria combinatorial optimization problems, submitted to Proceedings of the 15th International Conference on Multiple Criteria Decision Making, to appear.

    Google Scholar 

  8. Louis, S. J. and G. J. E. Rawlins, (1993), Pareto-optimality, GA-easiness and Deception. Proceedings of the International Conference on Genetic Algorithms, 118–123.

    Google Scholar 

  9. Murata, T., H. Ishibuchi, and H. Tanaka, (1996), Multi-objective genetic algorithm and its application to flowshop scheduling. Computers and Industrial Engineering, 30(4), 957–968.

    Article  Google Scholar 

  10. Myers, R. H. and D.C. Montgomery, (1995), Response Surface Methodology — Process and Product Optimization Using Designed Experiments, John Wiley & Sons, New York.

    Google Scholar 

  11. Pinedo, M., (1995), Scheduling: Theory, Algorithms, and Systems. Prentice Hall, New Jersey.

    Google Scholar 

  12. Schaffer, J.D., (1985), Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the First International Conference on Genetic Algorithms, 93–100.

    Google Scholar 

  13. Serifoglu, F. S. and G. Ulusoy, (1999), Parallel Machine Scheduling with Earliness and Tardiness Penalties. Computers & Operations Research, 26, 773–787.

    Article  MATH  MathSciNet  Google Scholar 

  14. Valenzuela-Rendon, M. and E. Uresti-Charre, (1997), A Non-Generational Genetic Algorithm for Multiobjective Optimization, Proceedings of the International Conference on Genetic Algorithms, pp 658–665

    Google Scholar 

  15. Viana, A. and J. P. Sousa, (2000). Using metaheuristics in multi-objective resource constrained project scheduling. European Journal of Operations Research, 120, 359–374.

    Article  MATH  Google Scholar 

  16. Zitzler, E. and L. Thiele, (Sep. 1998), Multiobjective Optimization Using Evolutionary Algorithms — A Comparative Case Study. In A. E. Eiben, Editor, Parallel Problem Solving from Nature V, 292–301, Amsterdam, Springer-Verlag.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering, Arizona State University, AZ 85287, P.O. Box 855906, Tempe

    W. Matthew Carlyle, Bosun Kim, John W. Fowler & Esma S. Gel

Authors
  1. W. Matthew Carlyle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bosun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John W. Fowler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Esma S. Gel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  2. Department of Mechanical Engineering Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, UP 208 016, Kanpur, India

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Matthew Carlyle, W., Kim, B., Fowler, J.W., Gel, E.S. (2001). Comparison of Multiple Objective Genetic Algorithms for Parallel Machine Scheduling Problems. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_33

Download citation

  • DOI: https://doi.org/10.1007/3-540-44719-9_33

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation