Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules

  • Conference paper
Current Topics in Artificial Intelligence (CAEPIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4177))

Included in the following conference series:

Abstract

The Job Shop Scheduling is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last years. In this work we propose a Genetic Algorithm hybridized with a local search method that searches over the space of semi-active schedules and a heuristic seeding method that generates active schedules stochastically. We report results from an experimental study over a small set of selected problem instances of common use, and also over a set of big problem instances that clarify the influence of each method in the Genetic Algorithm performance.

This work has been supported by project FEDER-MCYT TIC2003-04153 and by FICYT under grant BP04-021.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Bierwirth, C.: A Generalized Permutation Approach to Jobshop Scheduling with Genetic Algorithms. OR Spectrum 17, 87–92 (1995)

    MATH  Google Scholar 

  2. Carlier, J., Pinson, E.: Adjustment of heads and tails for the job-shop problem. European Journal of Operational Research 78, 146–161 (1994)

    Article  MATH  Google Scholar 

  3. Cheung, W., Zhou, H.: Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times. Annals of Operational Research 107, 65–81 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  4. Dell Amico, M., Trubian, M.: Applying Tabu Search to the Job-shop Scheduling Problem. Annals of Operational Research 41, 231–252 (1993)

    Article  MATH  Google Scholar 

  5. Giffler, B., Thomson, G.L.: Algorithms for Solving Production Scheduling Problems. Operations Reseach 8, 487–503 (1960)

    Article  MATH  Google Scholar 

  6. Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: Past, present and future. European Journal of Operational Research 113, 390–434 (1999)

    Article  MATH  Google Scholar 

  7. Mattfeld, D.C.: Evolutionary Search and the Job Shop. In: Investigations on Genetic Algorithms for Production Scheduling, Springer, Heidelberg (1995)

    Google Scholar 

  8. Varela, R., Vela, C.R., Puente, J., Gmez, A.: A knowledge-based evolutionary strategy for scheduling problems with bottlenecks. European Journal of Operational Research 145, 57–71 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  9. Varela, R., Serrano, D., Sierra, M.: New Codification Schemas for Scheduling with Genetic Algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 11–20. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Yamada, T., Nakano, R.: Scheduling by Genetic Local Search with multi-step crossover. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 960–969. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing Artificial Intelligence Center, University of Oviedo, Campus of Viesques, 33271, Gijón, Spain

    Miguel A. González, María Sierra, Camino R. Vela & Ramiro Varela

Authors
  1. Miguel A. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. María Sierra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Camino R. Vela
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ramiro Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dpto. de Ingeniería de la Información da las Comunicaciones, Universidad de Murcia, Campus de Espinardo, s/n., 30071, Murcia, Spain

    Roque Marín

  2. Universidad Politecnica de Valencia, (Spain)

    Eva Onaindía

  3. Dept. Electronics and Computer Science, University of Santiago de Compostela,  

    Alberto Bugarín

  4. Department of Life Sciences, Imperial College London, SW7 2AZ, London, UK

    José Santos

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

González, M.A., Sierra, M., Vela, C.R., Varela, R. (2006). Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_25

Download citation

  • DOI: https://doi.org/10.1007/11881216_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45914-9

  • Online ISBN: 978-3-540-45915-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation