Abstract
Optimization methods in discrete-event simulation have become widespread in numerous applications. However, the methods´ performance falls sharply in terms of computational time when more than one decision variable is handled. Current assay develops an adaptive genetic algorithm for the simulation optimization capable of achieving satisfactory results in time efficiency and response quality when compared to optimization software packages on the market. A series of experiments was elaborated to define the algorithm’s most significant parameters and to propose adaptations. According to the results, the most significant parameters are population size and number of generations. Further, adaptive strategies were proposed for these parameters which enabled the algorithm to obtain good results in response quality and time necessary to converge when compared to a commercial software package.
Highlights
Computational simulation is increasingly used to aid in decision-making (BANKS et al, 2009; LAW, 2007)
The optimization method proposed in current investigation was tested on the optimization of three discrete-event simulation models
The simulation models used are related to the allocation of resources in the manufacturing area, where the simulation is widely used to support decision-making (JAHANGIRIAN et al, 2010)
Summary
Computational simulation is increasingly used to aid in decision-making (BANKS et al, 2009; LAW, 2007). It has already been indicated as one of the most common research techniques in many areas, due to its versatility, flexibility and power of analysis (JAHANGIRIAN et al, 2010; RYAN; HEAVEY, 2006). Without the optimization software and in search of an optimal solution, simulation practitioners are forced to reconfigure their models to find that which presents the best system performance. This reality has changed due to accelerated computational capacity and improved. Banks et al (2009) and Fu (2002) report that the use of optimization with simulation has been growing continuously due to simulation software packages with integrated optimization routines
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