Stochastic customer order scheduling using simulation-based genetic algorithm

This study considers a dynamic customer order scheduling problem in a stochastic setting. Customer orders arrive at the service station dynamically and each consists of multiple product types with random workloads. Each order will be processed by a set of non-identical parallel servers. The objective is to determine the optimal workload assignment policy that minimizes the long-run expected order cycle time. A simulation-based genetic algorithm, named SimGA, is proposed to solve the problem, and a computable lower bound is developed for performance evaluation. Numerical experiments are reported to evaluate the performance of SimGA against two well-known simulation optimization methods.

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