Optimization of a Multi-Echelon Repair System Via Generalized Pattern Search with Ranking and Selection: A Computational Study
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With increasing developments in computer technology and available software, simulation is becoming a widely used tool to model, analyze, and improve a real world system or process. However, simulation in itself is not an optimization approach. Common optimization procedures require either an explicit mathematical formulation or numerous function evaluations at improving iterative points. Mathematical formulation is generally impossible for problems where simulation is relevant, which are characteristically the types of problems that arise in practical applications. Further complicating matters...
With increasing developments in computer technology and available software, simulation is becoming a widely used tool to model, analyze, and improve a real world system or process. However, simulation in itself is not an optimization approach. Common optimization procedures require either an explicit mathematical formulation or numerous function evaluations at improving iterative points. Mathematical formulation is generally impossible for problems where simulation is relevant, which are characteristically the types of problems that arise in practical applications. Further complicating matters is the variability in the simulation response which can cause problems in iterative techniques using the simulation model as a function generator. The mixed-variable generalized pattern search with ranking and selection (MGPS-RS) algorithm for stochastic response problems is applied to an external simulation model, by means of the NOMADm MATLAB software package. Numerical results are provided for several configurations of a simulation model representing a multi-echelon repairable problem containing discrete, continuous, and categorical variables. Computational experience results are presented.
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