Design and Ex ante Evaluation of an Architecture for Self-adaptive Model-Based DSS

  • Marcel-Philippe Breuer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


The quality of the decision support of a model-based Decision Support System (DSS) is fundamentally dependent on valid and actual models. A changing business environment can affect the validity of model components which could cause an incorrect model output. This problem is addressed in this paper by focusing on the self-adaptive property as a potential approach. To provide a model for decision support as close as possible to a dynamic business environment, the principles of self-adaptive systems are considered in an interconnected Model-/System-Controller (MoSyCo) architecture which is designed around DSS models. The design of the artifact is driven by a deduction of the problem characteristics to specify components of the intended architecture. The ex ante design evaluation is conducted in accordance to the stepwise evaluation by Sonnenberg and vom Brocke and considers a survey of 50 practitioners from the DSS domain.


Self-adaptive Model-based DSS Architecture DSR 


  1. 1.
    Power, D.J.: Decision Support, Analytics, and Business Intelligence. Business Expert Press, New York (2013)Google Scholar
  2. 2.
    Liang, T.P.: Critical success factors of decision support systems: an experimental study. ACM SIGMIS Database 17, 3–16 (1986)CrossRefGoogle Scholar
  3. 3.
    Sauter, V.L.: Decision Support Systems for Business Intelligence. Wiley, New Jersey (2010)zbMATHGoogle Scholar
  4. 4.
    Walker, W.E., Harremoes, P., Rotmans, J., van der Sluijs, J.P., van Asselt, M.B.A., Janssen, P., von Krauss, M.P.K.: Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integr. Assess. 4, 5–17 (2003)CrossRefGoogle Scholar
  5. 5.
    Oden, J.T., Prudhomme, S.: Control of modeling error in calibration and validation processes for predictive stochastic models. Int. J. Numer. Methods Eng. 87, 262–272 (2011)CrossRefGoogle Scholar
  6. 6.
    Liu, S., Duffy, A.H.B., Whitfield, R.I., Boyle, I.M.: Integration of decision support systems to improve decision support performance. Knowl. Inf. Syst. 22, 261–286 (2009)CrossRefGoogle Scholar
  7. 7.
    Laddaga, R.: Guest editor’s introduction: creating robust software through self-adaptation. IEEE Intell. Syst. 14, 26–29 (1999)CrossRefGoogle Scholar
  8. 8.
    Breuer, M.-P.: An architecture for self-adaptive model-based DSS illustrated by a reverse logistics scenario. In: Liu, S., Delibašić, B., Linden, I., Oderanti, F. (eds.) Proceedings of the 2nd EWG-DSS International Conference on Decision Support System Technology, Plymouth, p. 54 (2016)Google Scholar
  9. 9.
    Bossel, H.: Modeling and Simulation. A K Peters, Wellesley (1994)CrossRefGoogle Scholar
  10. 10.
    Salehie, M., Tahvildari, L.: Self-adaptive software: landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4, 14:1–14:42 (2009)CrossRefGoogle Scholar
  11. 11.
    Kovacevic, D., Mladenovic, N., Petrovic, B., Milosevic, P.: DE-VNS: Self-adaptive differential evolution with crossover neighborhood search for continuous global optimization. Comput. Oper. Res. 52, 157–169 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Teo, J.: Exploring dynamic self-adaptive populations in differential evolution. J. Soft Comput. 10, 673–686 (2006)CrossRefGoogle Scholar
  13. 13.
    Zhao, S.-Z., Suganthan, P.N., Das, S.: Self-adaptive differential evolution with multi-trajectory search for large-scale optimization. Soft Comput. 15, 2175–2185 (2011)CrossRefGoogle Scholar
  14. 14.
    Zhong, Y., Zhang, L.: Remote sensing image subpixel mapping based on adaptive differential evolution. IEEE Trans. Syst. Man Cybern. Part B 42, 1306–1329 (2012)CrossRefGoogle Scholar
  15. 15.
    Bäck, T.: Evolution strategies: an alternative evolutionary algorithm. In: Alliot, J., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) Artificial Evolution. AE 1995. LNCS, vol. 1063, pp. 1–20. Springer, Berlin (1996)CrossRefGoogle Scholar
  16. 16.
    Hoorfar, A.: Evolutionary programming in electromagnetic optimization: a review. IEEE Trans. Antennas Propag. 55, 523–537 (2007)CrossRefGoogle Scholar
  17. 17.
    Wang, C.-M., Huang, Y.-F.: Self-adaptive harmony search algorithm for optimization. Expert Syst. Appl. 37, 2826–2837 (2010)CrossRefGoogle Scholar
  18. 18.
    Meyyappan, L., Saygin, C., Dagli, C.H.: Real-time routing in flexible flow shops: a self-adaptive swarm-based control model. Int. J. Prod. Res. 45, 5157–5172 (2007)CrossRefGoogle Scholar
  19. 19.
    Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B., Tian, Q.: Self-adaptive learning based particle swarm optimization. Inf. Sci. 181, 4515–4538 (2011)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ji, X., Wei, Z., Feng, Y.: Effective vehicle detection technique for traffic surveillance systems. J. Vis. Commun. Image Represent. 17, 647–658 (2006)CrossRefGoogle Scholar
  21. 21.
    Brun, Y., Desmarais, R., Geihs, K., Litoiu, M., Lopes, A., Shaw, M., Smit, M.: A design space for self-adaptive systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 33–50. Springer, Berlin (2013)CrossRefGoogle Scholar
  22. 22.
    Hebig, R., Giese, H., Becker, B.: Making control loops explicit when architecting self-adaptive systems. In: Proceedings of the Second International Workshop on Self-organizing Architectures, pp. 21–28. ACM, New York (2010)Google Scholar
  23. 23.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. Manag. Inf. Syst. Q. 28, 75–105 (2004)CrossRefGoogle Scholar
  24. 24.
    Sonnenberg, C., vom Brocke, J.: Evaluations in the science of the artificial – reconsidering the build-evaluate pattern in design science research. In: Peffers, K., Rothenberger, M., Kuechler, B. (eds.) Design Science Research in Information Systems. Advances in Theory and Practice. DESRIST 2012. LNCS, vol. 7286, pp. 381–397. Springer, Berlin (2012)CrossRefGoogle Scholar
  25. 25.
    Baskerville, R.L., Pries-Heje, J.: Explanatory design theory. Bus. Inf. Syst. Eng. 2, 271–282 (2010)CrossRefGoogle Scholar
  26. 26.
    Pick, R.A., Weatherholt, N.: A review on evaluation and benefits of decision support systems. Rev. Bus. Inf. Syst. 17, 7–20 (2012)Google Scholar
  27. 27.
    Taylor, S.J., Letham, B.: Forecasting at scale. PeerJ Preprints (2017)Google Scholar
  28. 28.
    Ishwaran, H., Rao, J.S.: Spike and slab variable selection: frequentist and Bayesian strategies. Ann. Stat. 33, 730–773 (2005)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Ramirez, A.J., Cheng, B.H.C.: Design patterns for developing dynamically adaptive systems. In: Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp. 49–58. ACM, New York (2010)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of OsnabrückOsnabrückGermany

Personalised recommendations