On improving dimensioning in supply chain control by means of supply chain simulation

The task of planning and dimensioning complex production networks can be solved for most modern mass production systems by means of Optimal Dimensioning complex Networks (ODicoN) algorithms. Current versions of this dynamic optimisation approach described in (Klahold 1999) concentrate on continuous and deterministic production processes and are not prepared to manage stochastic error characteristics. Since interruptions of many relevant processes in complex production systems can only hardly be characterised by statistic parameters exclusively, it is crucial to integrate a simulation mechanism, which meets the very requirement of massive local evaluations needed for dynamic optimisation algorithms. We have developed an efficient simulation paradigm designed to gain more precise capacity information for sub-networks, which is crucial in dimensioning tasks.