Optimising a production process by a neural network/genetic algorithm approach

Abstract An important aspect of production control is the quality of the resulting end product. The end product should have optimal product characteristics and minimal faults. In theory, both objectives can be realised using an optimisation algorithm. However, the complexity of a production process may be very high. In most cases no mathematical function can be found to represent the production process. In this paper a method is presented to simulate a complex production process using a neural network. The subsequent optimisation is done by means of a genetic algorithm. The method is applied to the case study of a spinning (fibre-yarn) production process. The neural network is used to model the process, with the machine settings and fibre quality parameters as input, and the yarn tenacity (yarn strength) and elongation as output. The genetic algorithm is then used to optimise the input parameters for obtaining the best yarns. Since it is a multiobjective optimisation, the genetic algorithm is enforced with a sharing function and a Pareto optimisation. The paper shows that simultaneous optimisation of yarn qualities is easily achieved as a function of the necessary (optimal) input parameters, and that the results are considerably better than current manual machine intervention. The paper concludes by indicating future research towards making an optimal mixture of available fibre qualities.