A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection

In the automated manufacturing environment, different sets of alternative p rocess plans can normally be generated to manufacture each part. However, t his entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requ irements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternat ive process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the sele cted process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorit hm are introduced as possible approaches to solve such a problem. In partic ular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provid e a good quality solution to the process plan selection problem. The effect iveness of the proposed hybrid approach is illustrated by comparing its per formance with that of some published approaches and other optimization tech niques, by using several examples currently available in the literature, as well as a few randomly generated examples.