Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Simulation optimization with the linear move and exchange move optimization algorithm
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
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The purpose of this study is to investigate the feasibility of using a simulated annealing algorithm in conjunction with a simulation model to find the optimal parameter levels at which to operate the system being simulated. In particular, we discuss an effort to use simulated annealing to find a combination of input parameter values for an automated manufacturing system which optimizes a nonconvex, nonconcave objective function of the input parameters.This paper contains a brief description of an automated manufacturing system used to assemble three products. The problem objective is to maximize profit as a function of the levels of three parameters - batch size of arriving products, distribution of products in the batches, and machine output buffer size. Simulated annealing is then used to search for the optimal combination of input parameter levels. By experimenting with the simulated annealing parameters, the algorithm parameters are chosen such that the annealing program will consistently find the global optimum after evaluating approximately 36% of the input variable combinations.