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Abstract

The paper presents the conception of algorithm for scheduling of manufacturing systems with consideration of flexible resources and production routes. The proposed algorithm is based on ant colony optimisation (ACO) mechanisms. Although ACO metaheuristics do not guarantee finding optimal solutions, and their performance strongly depends on the intensification and the diversification parameters tuning, they are an interesting alternatives in solving NP hard problems. Their effectiveness and comparison with other methods are presented e.g. in [1, 4, 8]. The discussed search space is defined by the graph of operations planning relationships of the set of orders – the directed AND/OR-type graph describing precedence relations between all operations for scheduling. In the structure of the graph the notation ‘operation on the node’ is used. The presented model supports complex production orders, with hierarchical structures of processes and their execution according to both forward and backward strategies.

Keywords

Ant colony optimisation Scheduling And/or graphs 

References

  1. 1.
    Li, X., Shao, X., Gao, L., Qian, W.: An effective hybrid algorithm for integrated process planning and scheduling. Int. J. Prod. Econ. 126, 289–298 (2010)CrossRefGoogle Scholar
  2. 2.
    Leung, C.W., Wong, T.N., Mak, K.L., Fung, R.Y.K.: Integrated process planning and scheduling by an agent-based ant colony optimization. Comput. Ind. Eng. 59, 166–180 (2010)CrossRefGoogle Scholar
  3. 3.
    Chandra, B., Mohan, R.: Baskaran: a survey: ant colony optimization based recent research and implementation on several engineering domain. Expert Syst. Appl. 39, 4618–4627 (2012)CrossRefGoogle Scholar
  4. 4.
    Rossi, A., Dini, G.: Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robot. Comput. Integr. Manuf. 23, 503–516 (2007)CrossRefGoogle Scholar
  5. 5.
    Yagmahan, B., Yenisey, M.M.: A multi-objective ant colony system algorithm for flow shop scheduling problem. Expert Syst. Appl. 37, 1361–1368 (2010)CrossRefGoogle Scholar
  6. 6.
    Blum, Ch., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. Math. Model. Algorithms 3, 285–308 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. Evol. Comput. 6(4), 333–346 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Xing, L.-N., Chen, Y.-W., Wang, P., Zhao, Q.-S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10, 888–896 (2010)CrossRefGoogle Scholar
  9. 9.
    Pereira dos Santos, L., Vieira, G.E.I., Leite, H.V.R., Steiner, M.T.A.: Ant colony optimisation for backward production scheduling. Adv. Artif. Intell. 2012, Article ID 312132 (2012)Google Scholar
  10. 10.
    Dorigo, M., Maniezzo, V., Colorni, A.: Distributed optimization by ant colonies. In: Proceedings of ECAL91 – European Conference on Artificial Life, pp. 134–142. Elsevier Publishing, (1991)Google Scholar
  11. 11.
    Ponnambalam, S.G., Jawahar, N., Girish, B.S.: An Ant colony optimization algorithm for flexible job shop scheduling problem, New Advanced Technologies, ISBN: 978-953-307-067-4, InTech (2010)Google Scholar
  12. 12.
    Kato, E.R.R., Morandin Jr., O., Fonseca, M.A.S.: Ant colony optimization algorithm for reactive production scheduling problem in the job shop system. In: Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA (2009)Google Scholar
  13. 13.
    Chiang, Ch.-W., Huang, Y.-Q.: Multi-mode resource-constrained project scheduling by ant colony optimization with a dynamic tournament strategy. In: Third International Conference on Innovations in Bio-Inspired Computing and Applications (2012)Google Scholar
  14. 14.
    Diering, M., Dyczkowski, K., Hamrol, A.: New method for assessment of raters agreement based on fuzzy similarity. Adv. Intell. Syst. Comput. 368, 415–425 (2015)CrossRefGoogle Scholar
  15. 15.
    Lei, D.: Multi-objective production scheduling: a survey. Int. J. Adv. Manuf. Technol. 43, 926–938 (2009)CrossRefGoogle Scholar
  16. 16.
    Huang, R.-H., Yang, C.-L.: Overlapping production scheduling planning with multiple objectives - an ant colony approach. Int. J. Prod. Econ. 115, 163–170 (2008)CrossRefGoogle Scholar
  17. 17.
    Kalinowski, K., Zemczak, M.: Preparatory stages of the production scheduling of complex and multivariant products structures. Adv. Intell. Syst. Comput. 368, 475–483 (2015)CrossRefGoogle Scholar
  18. 18.
    Kalinowski, K., Grabowik, C., Kempa, W., Paprocka, I.: The graph representation of multivariant and complex processes for production scheduling. Adv. Mater. Res. 837, 422–427 (2014)CrossRefGoogle Scholar
  19. 19.
    Kalinowski, C. Grabowik, I. Paprocka, W. Kempa: Production scheduling with discrete and renewable additional resources. In: IOP Conference Series; Materials Science and Engineering; vol. 95, pp. 1757–8981 (2015)Google Scholar
  20. 20.
    Kalinowski, K., Grabowik, C., Kempa, W., Paprocka, I.: The procedure of reaction to unexpected events in scheduling of manufacturing systems with discrete production flow. Adv. Mater. Res. 1036, 1662–8985 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Engineering Processes Automation and Integrated Manufacturing Systems, Faculty of Mechanical EngineeringSilesian University of TechnologyGliwicePoland

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