Factory optimization using DES

Abstract: Discrete Event Simulation (DES) has been a powerful tool for modeling and optimization of production lines for many years. Developing detailed models may be very time consuming and might also be computationally costly to execute, especially if optimization techniques are to be applied. Aggregation techniques, simplifying a detailed system into fewer objects, can be an effective method to reduce the required computational resources as well as a way to shorten the development time. An aggregated model can be used to identify the main constraints in a system, dimensioning inter-line buffers, and focus development activities on the critical issues from a system performance perspective. An aggregated model of a real-world plant wide production system has already been developed in the discrete event simulation software FACTS Analyzer. Due to the limitations of FACTS Analyzer this model was instead rebuilt using the discrete event simulation software Plant Simulation which, while less user friendly, has much more configuration options. This model is then validated against the FACTS Analyzer model to ensure accurate results that can be implemented on a real life system. In addition some typical optimization experiments are carried out on the Plant Simulation model and the results analyzed. The first of these optimizations alters the buffer sizes to find the minimum amount of buffer capacity needed to get the minimum lead time while maintaining the highest possible throughput. The second optimization uses the same parameters but also adds alteration of the batch size. Both of these optimizations shows that it is possible to get better results with lower settings on both the buffer capacity and batch size then the default value. At the end of the report the project is evaluated and conclusions are drawn on what can be improved in the model, such as adding more streamlined updating of variant sequence.  Additionally future experiments that can be interesting to look at is specified based upon the results from the project.