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A survey on simulation optimization for the manufacturing system operation

Abstract Stochastic effects exist in a great number of manufacturing system operation problems. Simulation optimization methods are widely used for tackling the stochasticity. In order to provide a comprehensive coverage of simulation optimization publications with a focus on applications in manufacturing system operation logically, we classify the literature into two general categories of local optimization and global optimization. The local optimization literature is further divided into two subclasses based on the parameter spaces (discrete or continuous parameters). In each class, we explain how the corresponding methods integrated with simulation solve major manufacturing system operation problems, such as long- and short-term production planning, flow shop scheduling, and job shop scheduling. Finally, the current research status on simulation optimization for manufacturing operations is summarized. Meanwhile, some key issues, such as lack of unified problem benchmarks for comparison and low computational efficiency for real-scale problems, which need future research in this field, are discussed as well.