A genetic algorithm approach to optimization of asynchronous automatic assembly systems

This paper presents the application of genetic algorithms to the performance optimization of asynchronous automatic assembly systems (AAS). These stochastic systems are subject to blocking and starvation effects that make complete analytic performance modeling difficult. Therefore, this paper extends genetic algorithms to stochastic systems. The performance of the genetic algorithm is measured through comparison with the results of stochastic quasi-gradient (SQM) methods to the same AAS. The genetic algorithm performs reasonably well in obtaining good solutions (as compared with results of SQM) in this stochastic optimization example, even though genetic algorithms were designed for application to deterministic systems. However, the genetic algorithm's performance does not appear to be superior to SQM.

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