Skip to main content

Nachbarschaftssuche bei mehrkriteriellen Flow Shop Problemen

  • Chapter
Perspectives on Operations Research

Auszug

Das vorherrschende Paradigma der Ablaufplanung ist nach wie vor geprägt durch den klassischen Optimierungsansatz. Es werden diskrete (kombinatorische) Optimierungsprobleme betrachtet, die eine Zielfunktion unter Beachtung bestimmter Präzedenzbeziehungen und ggf. weiterer problemspezifischer Restriktionen optimieren. Dabei sind in den letzten Jahren erhebliche Fortschritte in der Entwicklung leistungsfähiger Lösungsalgorithmen erzielt worden (vgl. z. B. Brucker, 2001).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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.

Similar content being viewed by others

Literatur

  1. Basseur M, Seynhaeve F, Elghazali T (2002) Design of multi-objective evolutionary algorithms: Application to the flow-shop scheduling problem. In: Congress on Evolutionary Computation (CEC’2002), Band 2. IEEE Service Center, Piscataway NJ, May 2002, S. 1151–1156

    Google Scholar 

  2. Błażewicz J, Ecker KH, Pesch E, Schmidt G, Węglarz J (2001) Scheduling Computer and Manufacturing Processes, 2. Auflage. Springer Verlag, Berlin, Heidelberg, New York

    Google Scholar 

  3. Brucker P (2001) Scheduling Algorithms, 3. Auflage. Springer Verlag, Berlin

    Google Scholar 

  4. Conway RW, Maxwell WL, Miller LW (1967) Theory of Scheduling. Addison-Wesley, Reading, MA

    Google Scholar 

  5. Czyzak P, Jaszkiewicz A (1998) Pareto simulated annealing-a metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis 7:34–47

    Article  Google Scholar 

  6. Daniels RL, Chambers RJ (1990) Multiobjective flow-shop scheduling. Naval Research Logistics 37:981–995

    Article  Google Scholar 

  7. Finkel RA, Bentley JL (1974) Quad-trees. A data structure for retrieval on composite keys. Acta Informatica 4:1–9

    Article  Google Scholar 

  8. Glover F (1986) Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13(5):533–549

    Article  Google Scholar 

  9. Habenicht W (1984) Interaktive Lösungsverfahren für diskrete Vektoroptimierungsprobleme unter besonderer Berücksichtigung von Wegeproblemen in Graphen. Anton Hain, Königstein

    Google Scholar 

  10. Habenicht W (2000) Mehrzielkonzepte zur Unterstützung strategischer Entscheidungen. In: Foschiani S, Habenicht W, Schmid U und Wäscher G (Hrsg.) Strategisches Management im Zeichen von Umbruch und Wandel. Schäffer-Poeschel, Stuttgart, S. 175–195

    Google Scholar 

  11. Habenicht W, Scheubrein B, Scheubrein R (2002) Multiple criteria decision making. In: Derigs et al. (Hrsg.) Encyclopedia of Life Support Systems (EOLLS). EOLLS Publishers, Oxford

    Google Scholar 

  12. Holland JH (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  13. Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7(2):204–223

    Article  Google Scholar 

  14. Kirkpatrick S, Gellat CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  Google Scholar 

  15. Liao CJ, Yu WC, Joe CB (1997) Bicriterion scheduling the two-machine flowshop. Journal of the Operational Research Society 48:929–935

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Reeves CR (1999) Landscapes, operators and heuristic search. Annals of Operations Research 86:473–490

    Article  Google Scholar 

  18. Rinnooy Kan AHG (1976) Machine Scheduling Problems: Classification, Complexity and Computations. Martinus Nijhoff, The Hague

    Google Scholar 

  19. T’kindt V, Billaut JC (2002) Multicriteria Scheduling: Theory, Models and Algorithms. Springer Verlag, Berlin, Heidelberg, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Martin Morlock Christoph Schwindt Norbert Trautmann Jürgen Zimmermann

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Deutscher Universitäts-Verlag/GWV Fachverlage GmbH, Wiesbaden

About this chapter

Cite this chapter

Habenicht, W., Geiger, M.J. (2006). Nachbarschaftssuche bei mehrkriteriellen Flow Shop Problemen. In: Morlock, M., Schwindt, C., Trautmann, N., Zimmermann, J. (eds) Perspectives on Operations Research. DUV. https://doi.org/10.1007/978-3-8350-9064-4_4

Download citation

Publish with us

Policies and ethics