Adapting an evolutionary algorithm with embedded simulation and pseudo-random number generation for the cell broadband engine

For the problem of optimizing inspection strategies in multi-stage production systems, a metaheuristic consisting of an evolutionary algorithm with embedded simulation was developed in Van Volsem et al. (2007), Van Volsem (2009) and Van Volsem (accepted for publication, 2009). The metaheuristic requires normally distributed pseudo-random numbers; the time needed for this random number generation is a substantial fraction of the total computation time. In an effort to reduce the computation time, the metaheuristic was adapted for computation on the Cell Broadband Engine. The proposed adaptation is twofold: we propose a way to make the metaheuristic suitable for fast multicore computation, and secondly, the potential of SIMD computation for speeding up the random number generation process and the metaheuristic is investigated

Combining scripting and commercial simulation software to simulate in-plant logistics

In this paper we describe the use of a commercial discrete event simulation package (Siemens 2008) combined with a custom program, written in the programming language Python (Martelli 2006). Combining these two makes it possible to automatically generate a model for assembly line logistics simulation. The different stations of the assembly line, their connections and the storage near the assembly line were generated within seconds. A huge amount of time was saved compared with manual generation