Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011 pp 931–940Cite as

GA Based Scheduling of FMS Using Roulette Wheel Selection Process

  • Conference paper

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

Abstract

FMS Scheduling problem is one of the most difficult NP-hard combinatorial optimization problems.

Therefore, determining an optimal schedule and controlling an FMS is considered a difficult task. To achieve high performance for an FMS, a good scheduling system should make a right decision at a right time according to system conditions. It is difficult for traditional optimization techniques to provide the best solution. This paper focuses on the problems of determination of a schedule with the objective of minimizing the total make span time. An attempt has been made to generate a schedule using Genetic Algorithm with Roulette Wheel Base Selection Process.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Pinedo, M.: Scheduling: theory algorithms and systems. Prentice-Hall, Englewood (2002)

    MATH  Google Scholar 

  2. Li, D., Chen, L., Lin, Y.: Using functional virtual population as assistance to learn scheduling knowledge in dynamic manufacturing environment. International Journal of Production Research 41(17), 4011–4024 (2003)

    Article  Google Scholar 

  3. Li, D., Wu, C., Tong, K.: Using an unsupervised neural network and decision tree as knowledge acquisition tools for FMS scheduling. Int. J. Syst. Sci. 28(10), 977–985 (1997)

    Article  Google Scholar 

  4. Li, D., Han, K., Tong, K.: A Strategy for evolution of algorithms to increase the computational effectiveness of NP-hard scheduling problems. European Journal of Operation Research, 404–412 (1996)

    Article  Google Scholar 

  5. Li, D., She, I.: Using unsupervised learning technologies and simulation analysis to induce scheduling knowledge for flexible manufacturing systems. Internatioanal Journal of Production Research 32(9), 2187–2199 (1994)

    MATH  Google Scholar 

  6. Priore, P., Fuente, D., Gomez, A., Puente, J.: A review of machine learning in dynamic scheduling of flexible manufacturing systems. In: AIEDAM, vol. 15, pp. 251–263 (2001)

    MATH  Google Scholar 

  7. Quinlan, J.: Learning decision tree classifiers. ACM Computer Survey 28(1), 71 (1996)

    Article  Google Scholar 

  8. Shaw, M., Park, S., Raman, N.: Intelligent scheduling with machine learning capabilities: the induction of scheduling knowledge. IIE Trans. 24(2), 156–168 (1992)

    Article  Google Scholar 

  9. Pierreval, H., Ralambondrainy, H.: A simulation and learning technique for generating knowlege about manufacturing system. Journal of Operational Research Society 41(6), 461–474 (1990)

    Article  Google Scholar 

  10. Nakasuka, S., Yoshida, T.: Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool. Int. J. Production Research 30, 411–431 (1992)

    Article  Google Scholar 

  11. Chen, C.C., Yih, Y.: Identifying attributes for knowledge-based development in dynamic scheduling environments. International Journal of Production Research 34(6), 1739–1755 (1996)

    Article  Google Scholar 

  12. Sun, Y.L., Yih, Y.: An intelligent controller for manufacturing cells. Int J Prod Res 34(8), 2353–2373 (1996)

    Article  Google Scholar 

  13. Sabuncuoglu, I., Touhami, S.: Simulation metamodelling with neural networks: an experimental investigation. Int. J. Prod. Res. 40, 2483–2505 (2002)

    Article  Google Scholar 

  14. Jawahar, N., Aravindan, P., Ponnambalam, S.: A Genetic Algorithm for Scheduling Flexible Manufacturing Systems. Internation Journal of Advance Manufacturing Technology 14, 588–607 (1988)

    Article  Google Scholar 

  15. Davis, L.: Job Shop Scheduling with Genetic Algorithms. In: Proceedings of the International Conference on Genetic Algorithms and Their Applications, pp. 139–140 (1985)

    Google Scholar 

  16. Biegel, J.E., Davern, J.J.: Genetic Algorithm and Job Shop Scheduling. Computers and Industrial Engineering 19(1-4), 81–91 (1990)

    Article  Google Scholar 

  17. Nakano, R., Yamada, T.: Conventional Genetic Algorithm for Job Shop Problems. In: Fourth International Conference on Genetic Algorithms and Their Applications, San Diego, pp. 474–479 (1991)

    Google Scholar 

  18. Bierwirth, C.: A Generalized Permutation to Job Shop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)

    Article  Google Scholar 

  19. Croce, F., Tadei, R., Volta, G.: A Genetic Algorithm for the Job Shop Problem. Computers and Operations Research 22(1), 15–24 (1995)

    Article  Google Scholar 

  20. Dorndorf, U., Pesch, E.: Evolution Based Learning in a Job Shop Scheduling Environment. Computers and Operations Research 22(1), 25–40 (1995)

    Article  Google Scholar 

  21. Blazewicz, J., Domschke, W., Pesch, E.: The Job Shop Scheduling Problem: Conventional and New Solution Techniques. European Journal of Operational Research 93(1), 1–33 (1996)

    Article  Google Scholar 

  22. Giffle, B., Thompson, G.: Algorithms for solving production scheduling problems. International Journal of Operations Research 8, 487–503 (1960)

    Article  MathSciNet  Google Scholar 

  23. Nascimento, M.: Giffler and Thompson algorithm for job shop scheduling is still good for flexible manufacturing systems. Journal of Operational Research Society 44(5), 521–524 (1993)

    Article  Google Scholar 

  24. Kim, M., Kim, Y.: Simulation based real-time scheduling in FMS. Journal of Manufacturing System 13(2), 85–93 (1994)

    Article  Google Scholar 

  25. Kopfer, H., Mattfield, C.: A hybrid search algorithm for the job shop. In: Proceedings of the First International Conference on Operations and Quantitative Management, pp. 498–505 (1997)

    Google Scholar 

  26. Schultz, J., Mertens, P.: A comparison between an expert system, a GA and priority for production scheduling. In: Proceedings of the First International Conference on Operations and Quantitative Managemen, pp. 505–513 (1997)

    Google Scholar 

  27. Dorndorf, U., Pesche, E.: Combining genetic and local search for solving the job shop scheduling problem. In: APMOD 1993 Proceedings, Budapest, pp. 142–149 (1993)

    Google Scholar 

  28. Billo, R.E., Bidanda, B., Tate, D.: A genetic algorithm formulation of the cell formation problem. In: Proceedings of the 16 th International Conference on Computers and Industrial Engineering, pp. 341–344 (1994)

    Google Scholar 

  29. Holland, J.H.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1975)

    Google Scholar 

  30. Pinedo, M.: Scheduling: theory algorithms and systems. Prentice-Hall, Englewood (2002)

    Google Scholar 

  31. DeJong, K.A., Spears, W.M.: An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms. In: Proc. First Workshop Parallel Problem Solving from Nature, pp. 38–47. Springer, Berlin (1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IMS Engineering College, Ghaziabad, India

    Durgesh Sharma & Vijai Singh

  2. IGIT, IP University, Delhi, India

    Chitra Sharma

Authors
  1. Durgesh Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijai Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chitra Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Durgesh Sharma .

Editor information

Editors and Affiliations

  1. , Department of Mathematics, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Kusum Deep

  2. Department of Mathematics and, Computer Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

  3. , Department of Paper Technology, Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, 247667, India

    Millie Pant

  4. Information Technology and Management, ABV-Indian Institute of, 109, E- Block, Gwalior, 474010, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer India Pvt. Ltd.

About this paper

Cite this paper

Sharma, D., Singh, V., Sharma, C. (2012). GA Based Scheduling of FMS Using Roulette Wheel Selection Process. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_86

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-0491-6_86

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation