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• W2572399612 abstract "The optimal conditions evaluation in complex stochastic systems modelled through Discrete Event Simulation is often extremely costly in computational terms. Especially when the number of variables involved is high, as in the case of manufacturing systems, the duration of each simulation run can last even several hours of calculation. It therefore becomes very important to use an optimal search method that allows the experimenter to reduce as much as possible the number of function evaluations analysed. With this goal, the Authors compared the performance of a new nature-inspired Heuristic called Attraction Force Optimization (AFO), with those of traditional algorithms, applying these different methodologies to a real industrial case. The authors believe that the obtained results could be of great interest to the scientific community and the AFO heuristic can become a valuable reference for discrete event simulation-based optimization problems." @default.
• W2572399612 created "2017-01-26" @default.
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• W2572399612 date "2016-08-01" @default.
• W2572399612 modified "2023-09-26" @default.
• W2572399612 title "Optimization of Stochastic Discrete Event Simulation Models Using AFO Heuristic" @default.
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• W2572399612 doi "https://doi.org/10.1109/mcsi.2016.029" @default.
• W2572399612 hasPublicationYear "2016" @default.
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