Improving the remote scheduling of distributed production with process statistics and AI techniques

Abstract

Stochastic events, such as rush orders, stock-out events, and local failures have an important impact on the performance of distributed production, but they are difficult to anticipate and account for when scheduling production activities. Process statistics and artificial intelligence techniques can provide this knowledge to effectively time synchronization events among the simulation and scheduling federates of a same distributed architecture. Measurable benefits include reduced communication delays and, thus, improved responsiveness of the system to changes in production and new scheduling needs, as they arise. Comparative results on the productivity of actual industrial systems are proposed and discussed in the paper.