Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application

This paper presents a comparison of the performance of a fuzzy logic controlled genetic algorithm (FLC-GA) and a parameter tuned genetic algorithm (TGA) for an agile manufacturing application. In the FLC-GA, fuzzy logic controllers dynamically schedule parameters of the object-level GA. A fuzzy knowledge-base is automatically identified and tuned using a high-level GA. In the TGA, a high-level GA is used to determine an optimal static parameter set for the object-level GA. The object-level GA supports a global evolutionary optimization of design, manufacturing, and supplier planning decisions for manufacturing of printed circuit assemblies in an agile environment. We demonstrate that high-level system identification or tuning performed with small object-level search spaces, can be extended to more elaborate object-level search spaces. The TGA performs superior searches, but incurs large search times. The FLC-GA performs faster searches than a TGA, and is slower than the GA that utilizes a canonical static parameter set. However, search quality of the FLC-GA is comparable to that of the GA which utilizes a canonical static parameter set