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Natural Computing

, Volume 13, Issue 2, pp 179–192 | Cite as

A genetic algorithm for job-shop scheduling with operators enhanced by weak Lamarckian evolution and search space narrowing

  • Raúl Mencía
  • María R. SierraEmail author
  • Carlos Mencía
  • Ramiro Varela
Article

Abstract

The job-shop scheduling problem with operators is a very interesting problem that generalizes the classic job-shop problem in such a way that an operation must be algorithm to solve this problem considering makespan minimization. The genetic algorithm uses permutations with repetition to encode chromosomes and a schedule generation scheme, termed OG&T, as decoding algorithm. This combination guaranties that at least one of the chromosomes represents and optimal schedule and, at the samhat machines and operators are idle while an operation is available to be processed. To improve the quality of the schedules for large instances, we use Lamarckian evolution and modify the OG&T algorithm to further reduce the idle time of the machines and operators, in this case at the risk of leaving all optimal schedules out of the search space. We conducted a large experimental study showing that these improvements allow the genetic algorithm to reach high quality solutions in very short time, and so it is quite competitive with the current state-of-the-art methods.

Keywords

Job shop scheduling problem with operators Genetic algorithms Lamarckian evolution Schedule generation schemes 

Notes

Acknowledgments

This work has been supported by the Spanish Ministry of Science and Innovation under research project MICINN-FEDER TIN2010-20976-C02-02 and by FICYT under Grant BP09105.

References

  1. Agnetis A, Flamini M, Nicosia G, Pacifici A (2011) A job-shop problem with one additional resource type. J Sched 14(3):225–237CrossRefzbMATHMathSciNetGoogle Scholar
  2. Artigues C, Lopez P, Ayache P (2005) Schedule generation schemes for the job shop problem with sequence-dependent setup times: Dominance properties and computational analysis. Ann Oper Res 138:21–52CrossRefzbMATHMathSciNetGoogle Scholar
  3. Beasley JE (1990) Or-library: distributing test problems by electronic mail. J Oper Res Soc 41(11):1069–1072, http://www.jstor.org/stable/2582903 Google Scholar
  4. Bierwirth C (1995) A generalized permutation approach to jobshop scheduling with genetic algorithms. OR Spectrum 17:87–92CrossRefzbMATHGoogle Scholar
  5. Bierwirth C, Mattfeld DC (1999) Production scheduling and rescheduling with genetic algoritms. Evol Comput 7:1–17CrossRefGoogle Scholar
  6. Brucker P, Jurisch B, Sievers B (1994) A branch and bound algorithm for the job-shop scheduling problem. Discret Appl Math 49:107–127CrossRefzbMATHMathSciNetGoogle Scholar
  7. Dell’ Amico M., Trubian M. (1993) Applying tabu search to the job-shop scheduling problem. Ann Oper Res 41:231–252CrossRefzbMATHGoogle Scholar
  8. García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180:2044–2064CrossRefGoogle Scholar
  9. Giffler B, Thompson GL (1960) Algorithms for solving production scheduling problems. Oper Res 8:487–503CrossRefzbMATHMathSciNetGoogle Scholar
  10. González MA, Vela CR, Varela R (2008) A new hybrid genetic algorithm for the job shop scheduling problem with setup times. In: Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS-2008). AAAI Press, SidneyGoogle Scholar
  11. González MA, Vela CR, Varela R (2012) A competent memetic algorithm for complex scheduling. Nat Comput 11:151–160CrossRefzbMATHMathSciNetGoogle Scholar
  12. González Rodríguez I, Vela CR, Puente J, Varela R (2008) A new local search for the job shop problem with uncertain durations. In: Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS-2008). AAAI Press, SidneyGoogle Scholar
  13. Mattfeld DC (1995) Evolutionary search and the job shop investigations on genetic algorithms for production scheduling. Springer, BerlinGoogle Scholar
  14. Mencía C, Sierra MR, Varela R (2012) Depth-first heuristic search for the job shop scheduling problem. Ann Oper Res. doi: 10.1007/s10479-012-1296-x
  15. Mencía C, Sierra MR, Varela R (2013) Intensified iterative deepening A* with application to job shop scheduling. J Intell Manuf. doi: 10.1007/s10845-012-0726-6
  16. Mencía R, Sierra M, Mencía C, Varela R (2011) Genetic algorithm for job-shop scheduling with operators. Lect Notes Comput Sci 6687(2):305–314CrossRefGoogle Scholar
  17. Sierra MR, Mencía C, Varela R (2013) Searching for optimal schedules to the job-shop problem with operators. Technical report. iScOp Research Group. University of Oviedo, OviedoGoogle Scholar
  18. Sierra MR, Varela R (2010) Pruning by dominance in best-first search for the job shop scheduling problem with total flow time. J Intell Manuf 21(1):111–119CrossRefGoogle Scholar
  19. Trawiński B, Smetek M, Telec Z, Lasota T (2012) Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. Int J Appl Math Comput Sci 22(4):867–881zbMATHMathSciNetGoogle Scholar
  20. Van Laarhoven P, Aarts E, Lenstra K (1992) Job shop scheduling by simulated annealing. Oper Res 40:113–125CrossRefzbMATHMathSciNetGoogle Scholar
  21. Varela R, Serrano D, Sierra M (2005) New codification schemas for scheduling with genetic algorithms. Proceedings of IWINAC 2005. Lect Notes Comput Sci 3562:11–20CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

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

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