Advertisement

Behind the Scenes of Deadline24: A Memetic Algorithm for the Modified Job Shop Scheduling Problem

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 659)

Abstract

Job shop scheduling problem (JSSP) is an NP-hard optimization problem which has been widely studied in the literature due to its practical applicability. In this paper, we show how to model a workflow using a modified version of JSSP, in which a given operation may be executed on a number of different machines. Solving the instances of this modified JSSP, elaborated using our benchmark generation routine, constituted a qualifying task of the Deadline24 programming marathon. In the experimental study, we confront the results submitted by the participants with the solutions obtained using our memetic algorithms and other solvers. This analysis is backed up with the statistical tests.

Keywords

Job shop sheduling problem Memetic algorithm Workflow modeling Benchmark generation 

Notes

Acknowledgements

This research was supported by the Institute of Informatics (Silesian University of Technology) research grant no. BKM-507/RAU2/2016, and by the Polish National Centre for Research and Development under the Innomed Research and Development Grant No. POIR.01.02.00-00-0030/15.

References

  1. 1.
    Abdullah, S., Abdolrazzagh-Nezhad, M.: Fuzzy job-shop scheduling problems: a review. Inf. Sci. 278, 380–407 (2014)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Artigues, C., Belmokhtar, S., Feillet, D.: An exact solution algorithm for the job shop problem with sequence-dependent setup times. In: Régin, J.C., Rueher, M. (eds.) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. LNCS, vol. 3011, pp. 37–49. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Bhatt, N., Chauhan, N.R.: Genetic algorithm applications on job shop scheduling problem: a review. In: ICSCTI 2015, Faridabad, India, pp. 7–14 (2015)Google Scholar
  4. 4.
    Cinar, D., Topcu, Y.I., Oliveira, J.A.: A taxonomy for the flexible job shop scheduling problem. In: Migdalas, A., Karakitsiou, A. (eds.) Optimization, Control, and Applications in the Information Age. PROMS, vol. 130, pp. 17–37. Springer, Switzerland (2015)Google Scholar
  5. 5.
    Cwiek, M., Nalepa, J.: A fast genetic algorithm for the flexible job shop scheduling problem. In: GECCO 2014, Vancouver, Canada, pp. 1449–1450 (2014)Google Scholar
  6. 6.
    Cwiek, M., Nalepa, J., Dublanski, M.: How to generate benchmarks for rich routing problems? In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.P. (eds.) Intelligent Information and Database Systems. LNCS, vol. 9621, pp. 399–409. Springer, Heidelberg (2016)Google Scholar
  7. 7.
    Hosny, M.I., Mumford, C.L.: The single vehicle pickup and delivery problem with time windows: intelligent operators for heuristic and metaheuristic algorithms. J. Heuristics 16(3), 417–439 (2010)CrossRefMATHGoogle Scholar
  8. 8.
    Liu, F., Qi, Y., Xia, Z., Hao, H.: Discrete differential evolution algorithm for the job shop scheduling problem. In: GEC 2009, Shanghai, China, pp. 879–882 (2009)Google Scholar
  9. 9.
    Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: IEEE CEC 2016, Vancouver, Canada, pp. 209–216 (2016)Google Scholar
  10. 10.
    Nalepa, J., Blocho, M.: Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows. Soft. Comput. 20(6), 2309–2327 (2016)CrossRefGoogle Scholar
  11. 11.
    Nalepa, J., Cwiek, M., Kawulok, M.: Adaptive memetic algorithm for the job shop scheduling problem. In: IJCNN 2015, Killarney, Ireland, pp. 1–8 (2015)Google Scholar
  12. 12.
    Rahmati, S.H.A., Zandieh, M.: A new biogeography-based optimization (BBO) algorithm for the flexible job shop scheduling problem. Int. J. Adv. Manuf. Technol. 58(9), 1115–1129 (2012)CrossRefGoogle Scholar
  13. 13.
    Siminski, K.: Memetic neuro-fuzzy system with big-bang-big-crunch optimisation. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds.) Man-Machine Interactions 4. AISC, vol. 391, pp. 583–592. Springer, Switzerland (2015)Google Scholar
  14. 14.
    Thammano, A., Phu-ang, A.: A hybrid artificial bee colony algorithm with local search for flexible job-shop scheduling. Procedia Comput. Sci. 20, 96–101 (2013)CrossRefGoogle Scholar
  15. 15.
    Wang, L., Zhou, G., Xu, Y., Liu, M.: An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int. J. Adv. Manuf. Technol. 60(9), 1111–1123 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhang, C., Li, P., Guan, Z., Rao, Y.: A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Comput. Oper. Res. 34(11), 3229–3242 (2007)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Zhou, Y., Chen, H., Zhou, G.: Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem. Neurocomputing 137, 285–292 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland
  2. 2.Deadline24GliwicePoland
  3. 3.Future ProcessingGliwicePoland

Personalised recommendations