Many real-life systems in production and transportation logistics are complex, large-scale, and stochastic in nature. As a consequence, simheuristic approaches – which integrate simulation inside a metaheuristic framework – are becoming increasingly popular in the optimization and simulation communities. In a simheuristic algorithm, time-consuming simulation runs are required in order to: (i) obtain accurate results on the stochastic performance of solutions generated by the metaheuristic; and (ii) provide feedback that can be useful to better guide the metaheuristic search. If the underlying system is complex, discrete-event simulation might be needed, and then the simulation component could easily overrun the computational time of the metaheuristic component. Thus, for each new solution generated by the metaheuristic, several related questions arise: (i) should the simulation component be applied to that solution? – i.e., is that solution ‘promising’ enough to invest additional computational time on retrieving information about its performance in a stochastic environment?; and (ii) if so, how many simulation runs are needed in order to obtain useful information (i.e., statistics with a minimum level of accuracy)? This paper discusses these issues and proposes several concepts that allow to improve the efficiency (in terms of computational time) of simheuristic algorithms. A case study, based on a typical manufacturing system, is introduced. In order to illustrate and test different speeding-up techniques, the system is optimized by using a simheuristic that integrates a genetic algorithm with discrete-event simulation.

生产和运输物流中的许多现实系统本质上都是复杂的，大规模的和随机的。结果，将模拟整合到元启发式框架中的模拟方法在优化和模拟社区中变得越来越流行。在模拟启发式算法中，需要耗时的模拟运行，以便：（i）获得关于由元启发式方法生成的解的随机性能的准确结果；和（ii）提供有助于更好地指导元启发式搜索的反馈。如果底层系统很复杂，则可能需要进行离散事件模拟，然后模拟组件很容易超出元启发式组件的计算时间。因此，对于由元启发法生成的每个新解决方案，都会出现几个相关的问题：（i）是否应将模拟组件应用于该解决方案？–即，该解决方案是否“有前途”，足以花费更多的计算时间来检索有关其在随机环境中的性能的信息？和（ii）如果是这样，需要多少次模拟运行才能获得有用的信息（即，具有最低准确性的统计信息）？本文讨论了这些问题，并提出了一些概念，这些概念可以提高模拟启发式算法的效率（在计算时间方面）。介绍了基于典型制造系统的案例研究。为了说明和测试不同的加速技术，通过使用将遗传算法与离散事件模拟集成在一起的模拟方法对系统进行了优化。