Application of Fuzzied Genetic Algorithms in Optimizing Parameters in a Manufacturing System for Resource Allocation

the fuzzy centriod method. Genetic algorithms (GA). since first introduced by Holland [I] in early 1970s. was applied on optimization problems and showed significant performance. However, it was seldom mentioned in manufacturing literature, especially in optimizing resources allocation problems and line balancing problems. This paper is concerned with applying the Genetic Algorithms technique, interfacing with the simulation software, WITNESS. to perform experiments in order to obtain a set of parameters for determining the resource level in setting up a manufacturing systems under different management strategies. In addition. Fuzzy Logic technique, which makes a contribution to minimize human supervision during GAS routine use as an optimization tool. is also integrated into the whole system to increase the efficiency and reliability of this AI tool. The method described in this paper is to corporate fuzzied genetic algorithms (FGA) for optimizing parameters using C++ programming techniques, and then applying simulation as the optimizing parameters evaluation tools. By using this techniques, optimal parameters for a manufacturing system can be determined and beneficial to resource allocation and eventually order taking of the organization. The method used here is to corporate Fuzzied Genetic Algorithm (FGX’s) for optimizing parameters using C++ programming technique, and then applying simulation using WITNESS as the result parameters evaluation tools. A random number generator for experimental parameters would also be constructed and utilized for construction of initial population. When the first program is finished. it would be required to validate the method of FGA's on manufacturing optimization. Here. the parameters are the inputs to simulation model, and each changeable parameter behaves as a piece of gene.