An Extended Deterministic Dendritic Cell Algorithm for Dynamic Job Shop Scheduling

  • X.N. Qiu
  • H.Y.K. Lau
Conference paper


The problem of job shop scheduling in a dynamic environment where random perturbation exists in the system is studied. In this paper, an extended deterministic Dendritic Cell Algorithm (dDCA) is proposed to solve such a dynamic Job Shop Scheduling Problem (JSSP) where unexpected events occurred randomly. This algorithm is designed based on dDCA and makes improvements by considering all types of signals and the magnitude of the output values. To evaluate this algorithm, ten benchmark problems are chosen and different kinds of disturbances are injected randomly. The results show that the algorithm performs competitively as it is capable of triggering the rescheduling process optimally with much less run time for deciding the rescheduling action. As such, the proposed algorithm is able to minimize the rescheduling times under the defined objective and to keep the scheduling process stable and efficient.


Artificial Immune System Slack Time Machine Breakdown Dendritic Cell Algorithm Danger Theory 
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  1. 1.
    Rangsaritratsamee, R., Ferrell, J.W.G., Kurz, M.B.: Dynamic rescheduling that simultaneously considers efficiency and stability. Computers & Industrial Engineering. Vol. 46, pp. 1-15 (2004).CrossRefGoogle Scholar
  2. 2.
    Jain, A., Meeran, S.: A state-of-the-art review of job-shop scheduling techniques. European Journal of Operations Research. Vol. 113, pp. 390-434 (1999).MATHCrossRefGoogle Scholar
  3. 3.
    Vinod, V., Sridharan, R.: Dynamic job-shop scheduling with sequence-dependent setup times: simulation modeling and analysis. International Journal of Advanced Manufacturing Technology. Vol. 36, pp. 355-372 (2008).CrossRefGoogle Scholar
  4. 4.
    Xiang, W., Lee, H.P.: Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence. Vol. 21, pp. 73-85 (2008).CrossRefGoogle Scholar
  5. 5.
    Subramaniam, V., Ramesh, T., Lee, G.K., Wong, Y.S., Hong, G.S.: Job shop scheduling with dynamic fuzzy selection of dispatching rules. International Journal of Advanced Manufacturing Technology. Vol. 16, pp. 759-764 (2000).CrossRefGoogle Scholar
  6. 6.
    Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research. Vol. 20, pp. 27-45 (1982).CrossRefGoogle Scholar
  7. 7.
    Kang, S.G.: Multi-agent based beam search for intelligent production planning and scheduling. PhD Thesis, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong (2007).Google Scholar
  8. 8.
    Jang, W.S.: Dynamic scheduling of stochastic jobs on a single machine. European Journal of Operational Research. Vol. 138, pp. 518-530 (2002).MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Sabuncuoglu, I., Bayiz M.: Analysis of reactive scheduling problems in a job shop environment. European Journal of Operational Research. Vol. 126, pp. 567-586 (2000).MATHCrossRefGoogle Scholar
  10. 10.
    De Castro, L.N., Timmis, J.: Artificial Immune Systems: A new computational intelligence approach. Springer, New York (2002).MATHGoogle Scholar
  11. 11.
    Greensmith, J., Aicklin, W., Cayzer, S.: Introducing dendritic cells as a novel immuneinspired algorithm for anomaly detection. 4th International Conference on Artificial Immune Systems. Vol. 3627, pp. 153-167 (2005).Google Scholar
  12. 12.
    Mascis, A., Pacciarelli, D.: Job-shop scheduling with blocking and no-wait constraints. European Journal of Operational Research. Vol. 143, pp. 498-517 (2002).MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Qiu, X.N., Lau, H.Y.K.: An AIS-based hybrid algorithm with PSO for job shop scheduling problem. 10th IFCA Workshop on Intelligent Manufacturing Systems. pp. 371-376 (2010).Google Scholar
  14. 14.
    Garrett, S.M.: How do we evaluate artificial immune systems? Evolutionary Computation. Vol. 13, pp. 145-177 (2005).CrossRefMathSciNetGoogle Scholar
  15. 15.
    Aickelin, U., Bentley, P., Cayzer, S., Kim, J., McLeod, J.: Danger theory: The link between AIS and IDS? 2th International Conference on Artificial Immune Systems. Vol. 2787, pp. 147-155 (2003).CrossRefGoogle Scholar
  16. 16.
    Al-Hammadi, Y., Aickelin, U., Greensmith, J.: DCA for Bot Detection. 2008 IEEE World Congress on Computational Intelligence. pp. 1807-1816 (2008).Google Scholar
  17. 17.
    Greensmith, J.: The dendritic cell algorithm. PhD Thesis, School of Computer Science, University of Nottingham, UK (2007).Google Scholar
  18. 18.
    Li, X., Fu, H.D., Huang, S.L.: Design of a dendritic cells inspired model based on danger theory for intrusion detection system. Proceedings of 2008 IEEE International Conference on Networking, Sensing and Control. Vol. 2, pp. 1137-1141 (2008).CrossRefGoogle Scholar
  19. 19.
    Greensmith, J., Aickelin, U.: The deterministic dendritic cell algorithm. 7th International Conference on Artificial Immune Systems. Vol. 5132, pp. 291-302 (2008).CrossRefGoogle Scholar
  20. 20.
    Beasley, J.: OR-Library: Distributing test problems by electronic mail. The Journal of the Operational Research Society. Vol. 41, pp. 1069-1072 (1990).Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Industrial and Manufacturing Systems Engineering DepartmentThe University of Hong KongHong KongP.R. China

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