Optimising an assembly line through simulation augmented by genetic algorithms

Although simulation modelling can be used to model assembly lines, it is unable to optimise the line operations. Optimisation techniques, such as generic algorithms, must be employed to optimise objective functions such as the throughput of the line, machine utilisation and tardiness. This paper discusses how a serial assembly line for compressors was modelled using simulation alien, and optimised by genetic algorithms In the paper, the influence of the size of the population, the crossover probability, the mutation probability and the number of elite chromosomes on the performance of the genetic algorithm are discussed. The results of the analysis of line throughput revealed that the line is well-balanced, and that some machines can work at a slower rate without compromising the maximum expected throughput. In addition, it was found that machine speed is not a determinant of optimum throughput. It was established that machine utilisation can be improved by another 4.3%, indicating that the machines were already well used. On the other hand, tardiness was improved by 23% by slowing down the arrival of the compressor blocks.