The Role of Fuzzy and Genetic Algorithms in Part Family Formation and Sequence Optimisation for Flexible Manufacturing Systems

Group technology (GT) is one of the key issues in the successful implementation of flexible manufacturing systems (FMSs). The success of GT implementation is in the effective formation of part families (PFs) and similarity coefficients measures. Over the past three decades, many similarity coefficients have been proposed, but a better similarity coefficient measure is required. The decision-making process in a manufacturing system often involves uncertainties and ambiguities. Under such circumstances, fuzzy methodologies have proved to be an effective tool for taking fuzziness into consideration. The first part of this paper deals with the fuzzy part-family formation. This was achieved in the following ways:1. A new similarity coefficient measure has been developed and this coefficient measure is used to form a part-family.2. A mathematical model that uses this similarity coefficient for solving the part-family formation problems optimally in an FMS is developed.The fuzzy approach has the special advantage of producing more accurate results than conventional clustering and other methods. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part family. This will give a balanced work load for the machine. In the second part of this paper, the introduction of the concept of genetic algorithms is proposed to eliminate more job sequences and, finally, the optimum sequence is obtained through the minimum penalty cost. Software is developed and implemented to obtain an optimum sequence and, finally, a numerical example is given as an illustration.