A simulation optimization approach to design field service systems with remote diagnostics for semiconductor equipment maintenance

With advances in information technology, service activities for expensive equipment used in semiconductor manufacturing can be performed from a remote location. This capability is called remote diagnostics (RD). In this dissertation, a queueing-location model is developed to analyze the capacity and location problem of after sales service providers, considering the effects of RD technology. This model optimizes the location, capacity and the type of service centers while taking congestion effects into consideration. The model is solved using a simulation optimization approach in which we use a genetic algorithm to search the solution space. For speeding up the simulation optimization process a heuristic for ranking different solution alternatives using short transient simulations is also developed. Use of this heuristic as the ranking mechanism in the genetic algorithm reduces simulation optimization run times up to 95% compared with the traditional approaches in the simulation literature.