Neuro-Fuzzy Method to Forecast Behavior of Material-flow Simulations for Assembly Lines

H.K. Tönshoff, M. Manns, and K. Spardel (Germany)


Modelling and Simulation Methods, MaterialFlow Simulation, NeuroFuzzy, Production Planning


Flexible production lines are often planned in dynamic environment with very demanding time schedules. Since simulations require extensive modeling time, material-flow simulation is mainly used in later stages of the planning process. As are many artificial intelligence methods, neuro-fuzzy systems like CANFIS can be interpreted as fuzzy rules. Their use as a material-flow forecasting tool for early planning stages could support the planner with comprehensible estimates to improve the planning process. However, for a variety of hybrid neuro-fuzzy systems, including CANFIS, high input parameter dimensionality typical for material-flow simulations lead to unacceptable computing times. This paper proposes a method using CANFIS for learning fuzzy rules from simulation data. Results are given concerning if and how well the neuro-fuzzy algorithm CANFIS learns to transform simulation model parameters to performance measures.

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