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Hybrid Agent-Based Modeling (HABM)—A Framework for Combining Agent-Based Modeling and Simulation, Discrete Event Simulation, and System Dynamics

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Operations Research Proceedings 2017

Part of the book series: Operations Research Proceedings ((ORP))

Abstract

The hybrid agent-based modeling (HABM) framework is intended to specify integrated models based on agent-based modeling and simulation, discrete event simulation, and system dynamics. HABM not only supports the specification of agents exhibiting discrete and continuous behavior but also considers flexible structures. The latter is an important aspect for many agent-based models. HABM has been successfully used in strategic workforce planning. However, it can also be applied to other OR fields such as supply chain management or even for the specification of certain kinds of agent-based metaheuristics.

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References

  1. Kahneman, D., Rosenfield, A.M., Gandhi, L., & Blaser, T. (2016, October). Noise: how to overcome the high, hidden cost of inconsistent decision making. Harvard Business Review, 36–43.

    Google Scholar 

  2. Blum, Ch., Puchinger, J., Raidl, G. R., & Roli, A. (2011). Hybrid metaheuristics in combinatorial optimization: a survey. Applied Soft Computing, 11, 4135–4151.

    Article  Google Scholar 

  3. Sörensen, K., & Glover, F. W. (2013). Metaheuristics. In S. I. Gass & M. C. Fu (Eds.), Encyclopedia of Operations Research and Management Science (3rd ed., pp. 960–970). New York: Springer.

    Chapter  Google Scholar 

  4. Anderson, D. R., Sweeney, D. J., Williams, Th A, Camm, J. D., Cochran, J. J., Fry, M. J., et al. (2016). An Introduction to Management Science - Quantitative Approaches to Decision Making (14th ed.). Mason: South-Western.

    Google Scholar 

  5. Sterman, J. D. (2000). Business Dynamics - Systems Thinking and Modeling for a Complex World. Boston: McGraw-Hill.

    Google Scholar 

  6. Lättilä, L., Hilletofth, P., & Lin, B. (2010). Hybrid simulation models - when, why, how? Expert Systems with Applications, 37(12), 7969–7975.

    Article  Google Scholar 

  7. Moon, Y. B. (2017). Simulation modeling for sustainability: a review of the literature. International Journal of Sustainable Engineering, 10(1), 2–19.

    Article  Google Scholar 

  8. Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistic and supply chain context. Decision Support Systems, 52(4), 802–815.

    Article  Google Scholar 

  9. Brailsford, S. C., Harper, P. R., & Pitt, M. (2009). An analysis of the academic literature on simulation and modelling in healthcare. Journal of Simulation, 3, 130–140.

    Article  Google Scholar 

  10. Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115–152.

    Article  Google Scholar 

  11. Witsenhausen, H. S. (1966). A class of hybrid-state continuous-time dynamic systems. IEEE Transactions on Automatic Control, 11(2), 161–167.

    Article  Google Scholar 

  12. Johansson, K. H., Egerstedt, M., Lygeros, J., & Sastry, S. (1999). On the regularization of Zeno hybrid automata. System and Control Letters, 38, 141–150.

    Article  Google Scholar 

  13. Zeigler, B. P., Praehofer, H., & Kim, T. G. (2000). Theory of Modeling and Simulation - Integrating Discrete Event and Continuous Complex Dynamic Systems (2nd ed.). San Diego: Academic Press.

    Google Scholar 

  14. Block, J. (2016). A hybrid modeling approach for incorporating behavioral issues into workforce planning. In: IEEE International Conference on Systems, Man, and Cybernetics (pp. 326–331).

    Google Scholar 

  15. Block, J., & Pickl, S. (2014). The mystery of job performance: a system dynamics model of human behavior. In: 32nd International Conference of the System Dynamics Society.

    Google Scholar 

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Correspondence to Joachim Block .

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Block, J. (2018). Hybrid Agent-Based Modeling (HABM)—A Framework for Combining Agent-Based Modeling and Simulation, Discrete Event Simulation, and System Dynamics. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_80

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