Abstract
Agent-based simulation is increasingly used in industry to model systems of interest allowing the evaluation of alternative scenarios. By this means, business managers can estimate the consequences of policy changes at low cost before implementing them in the business. However, in order to apply such models with confidence, it is necessary to validate them continuously against changing business patterns. Typically, models contain key parameters which significantly affect the overall behaviour of the system. The process of selecting such parameters is an inverse problem known as ‘tuning’ In this chapter, we describe the application of computational intelligence to tune the parameters of a workforce dynamics simulator. We show that the best algorithm achieves reduced tuning times as well as more accurate field workforce simulations. Since implementation, this algorithm has facilitated the use of simulation to assess the effect of changes in different business scenarios and transformation initiatives.
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Notes
- 1.
This time was measured on an Intel(R) Core(TM) i5 CPU M 520 @ 2.40 GHZ, 1.86 GB of RAM.
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Mamer, T., McCall, J., Shakya, S., Owusu, G., Regnier-Coudert, O. (2013). Understanding Team Dynamics with Agent-Based Simulation. In: Owusu, G., O’Brien, P., McCall, J., Doherty, N. (eds) Transforming Field and Service Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44970-3_12
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