On the Evolutionary-Fuzzy Control of WIP in Manufacturing Systems

The effectiveness of optimized fuzzy controllers in the production scheduling has been demonstrated in the past through the extensive use of Evolutionary Algorithms (EA) for the Work-In-Process (WIP) reduction. The EA strategy tunes a set of distributed fuzzy control modules whose objective is to control the production rate in a way that satisfies the demand for final products, while reducing WIP within the production system. The EA identifies optimal design solutions in a given search space. How robust and generic is the controller that comes out of this process? This paper faces this question by testing the evolutionary tuned fuzzy controllers in demand conditions other than the ones used for their optimization. The evolutionary-fuzzy controllers are also compared to heuristically designed ones. Extensive simulations of production lines and networks show that the evolutionary-fuzzy strategy achieved a substantial reduction of WIP compared to the heuristic approach in all test cases.