A GENETIC ALGORITHM APPROACH TO OPTIMIZATION OF ASYNCHRONOUS AUTOMATIC ASSEMBLY SYSTEMS

This paper presents the application of genetic algorithms to the perfo rmance optimization of asynchronous automatic assembly systems (AAS). These stochastic systems are subject to blocking and starvation effect s that make complete analytic performance modeling difficult. Therefor e, this paper extends genetic algorithms to stochastic systems. The pe rformance of the genetic algorithm is measured through comparison with the results of stochastic quasi-gradient (SQM) methods to the same AA S. The genetic algorithm performs reasonably well in obtaining good so lutions (as compared with results of SQM) in this stochastic optimizat ion example, even though genetic algorithms were designed for applicat ion to deterministic systems. However, the genetic algorithm's perform ance does not appear to be superior to SQM.