Genetic Algorithm for the Job-Shop Scheduling with Skilled Operators

  • Raúl Mencía
  • María R. Sierra
  • Ramiro Varela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)

Abstract

In this paper, we tackle the job shop scheduling problem (JSP) with skilled operators (JSPSO). This is an extension of the classic JSP in which the processing of a task in a machine has to be assisted by one operator skilled for the task. The JSPSO is a challenging problem because of its high complexity and because it models many real-life situations in production environments. To solve the JSPSO, we propose a genetic algorithm that incorporates a new coding schema as well as genetic operators tailored to dealing with skilled operators. This algorithm is analyzed and evaluated over a benchmark set designed from conventional JSP instances. The results of the experimental study show that the proposed algorithm performs well and at the same time they allowed us to gain insight into the problem characteristics and to draw ideas for further improvements.

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References

  1. 1.
    Agnetis, A., Flamini, M., Nicosia, G., Pacifici, A.: A job-shop problem with one additional resource type. J. Scheduling 14(3), 225–237 (2011)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Agnetis, A., Murgia, G., Sbrilli, S.: A job shop scheduling problem with human operators in handicraft production. International Journal of Production Research 52(13), 3820–3831 (2014)CrossRefGoogle Scholar
  3. 3.
    Bierwirth, C.: A generalized permutation approach to job shop scheduling with genetic algorithms. OR Spectrum 17, 87–92 (1995)CrossRefMATHGoogle Scholar
  4. 4.
    Dell’ Amico, M., Trubian, M.: Applying tabu search to the job-shop scheduling problem. Annals of Operational Research 41, 231–252 (1993)CrossRefMATHGoogle Scholar
  5. 5.
    García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180, 2044–2064 (2010)CrossRefGoogle Scholar
  6. 6.
    Mencía, R., Sierra, M.R., Mencía, C., Varela, R.: A genetic algorithm for job-shop scheduling with operators enhanced by weak lamarckian evolution and search space narrowing. Natural Computing 13(2), 179–192 (2014)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Van Laarhoven, P., Aarts, E., Lenstra, K.: Job shop scheduling by simulated annealing. Operations Research 40, 113–125 (1992)CrossRefMATHMathSciNetGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Raúl Mencía
    • 1
  • María R. Sierra
    • 1
  • Ramiro Varela
    • 1
  1. 1.Department of Computer ScienceUniversity of OviedoGijónSpain

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