Skip to main content
SpringerLink
Log in

Multi-Objective Optimization for Dynamic Single-Machine Scheduling

  • Conference paper
Book cover Advances in Swarm Intelligence (ICSI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6729))

Included in the following conference series:

Abstract

In this paper, a multi-objective evolutionary algorithm based on gene expression programming (MOGEP) is proposed to construct scheduling rules (SRs) for dynamic single-machine scheduling problem (DSMSP) with job release dates. In MOGEP a fitness assignment scheme, diversity maintaining strategy and elitist strategy are incorporated on the basis of original GEP. Results of simulation experiments show that the MOGEP can construct effective SRs which contribute to optimizing multiple scheduling measures simultaneously.

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. Balas, E.: Machine scheduling via disjunctive graphs: an implicit enumeration algorithm. Oper. Res. 17, 941–957 (1969)

    Article  MATH  Google Scholar 

  2. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  3. Laguna, M., Barnes, J., Glover, F.: Tabu search methods for a single machine scheduling problem. J. Intell. Mauf. 2, 63–74 (1991)

    Article  Google Scholar 

  4. Jakobović, D., Budin, L.: Dynamic Scheduling with Genetic Programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 73–84. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Atlan, L., Bonnet, J., Naillon, M.: Learning Distributed Reactive Strategies by Genetic Programming for the General Job Shop Problem. In: 7th Annual Florida Artificial Intelligence Research Symposium. IEEE Press, Florida (1994)

    Google Scholar 

  6. Miyashita, K.: Job-shop Scheduling with Genetic Programming. In: Genetic and Evolutionary Computation Conference, pp. 505–512. Morgan Kaufmann, San Fransisco (2000)

    Google Scholar 

  7. Nie, L., Shao, X.Y., Gao, L., Li, W.D.: Evolving Scheduling Rules with Gene Expression Programming for Dynamic Single-machine Scheduling Problems. Int. J. Adv. Manuf. Tech. 50, 729–747 (2010)

    Article  Google Scholar 

  8. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE T. Evolut. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  9. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Nondominated Sorting Genetic Algorithm for Mmulti-objective Optimization: NSGA-II. In: Schoenauer, M., Deb, K., Rudolph, G., Yao, X., Lutton, E., Merelo, J.J., Schwefel, H.-P. (eds.) Parallel Problem Solving from Nature – PPSN VI, pp. 849–858. Springer, Berlin (2000)

    Chapter  Google Scholar 

  10. Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann, California (1993)

    Google Scholar 

  11. Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: 1st IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, pp. 82–87. IEEE Press, New Jersey (1994)

    Google Scholar 

  12. Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  13. Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  14. Kacem, I., Hammadi, S., Borne, P.: Pareto-optimality Approach for Flexible Job-shop Scheduling Problems: Hybridization of Evolutionary Algorithms and Fuzzy Logic. Math. Comput. Simulat. 60, 245–276 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  15. Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex System 13(2), 87–129 (2001)

    MathSciNet  MATH  Google Scholar 

  16. Ferreira, C.: Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 50–60. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Zou, C., Nelson, P.C., Xiao, W., Tirpak, T.M.: Discovery of Classification Rules by Using Gene Expression Programming. In: International Conference on Artificial Intelligence, Las Vegas, pp. 1355–1361 (2002)

    Google Scholar 

  18. Zuo, J., Tang, C., Li, C., Yuan, C., Chen, A.: Time Series Prediction Based on Gene Expression Programming. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 55–64. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Chen, Y., Tang, C., Zhu, J.: Clustering without Prior Knowledge Based on Gene Expression Programming. In: 3rd International Conference on Natural Computation, pp. 451–455 (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, People’s Republic of China

    Li Nie, Liang Gao, Peigen Li & Xiaojuan Wang

Authors
  1. Li Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peigen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123, Suzhou, China

    Yuhui Shi

  3. Automation College, Chongqing University, 400030, Chongqing, China

    Yi Chai

  4. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, P.R. China

    Guoyin Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nie, L., Gao, L., Li, P., Wang, X. (2011). Multi-Objective Optimization for Dynamic Single-Machine Scheduling. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21524-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21523-0

  • Online ISBN: 978-3-642-21524-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation