Work-in-process scheduling by evolutionary tuned distributed fuzzy controllers

This paper presents an evolutionary algorithm (EA) strategy for the optimization of generic work-in-process (WIP) scheduling fuzzy controllers. The EA strategy is used to tune a set of distributed fuzzy control modules that are responsible for WIP scheduling. The objective is to control the production rate in each processing stage in a way that satisfies the demand for final products, while reducing WIP within the production system. The EA identifies the parameters for which a set of fuzzy controllers performs optimal with respect to WIP and backlog minimization. The proposed EA strategy is compared to heuristically tuned distributed fuzzy controllers. Extensive simulation testing shows that the EA strategy significantly improves the system´s performance