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An Entropy Measure of Flow Dominance for Predicting Operations Performance

机译：流量支配性的熵测度用于预测运营绩效

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摘要

The flow of jobs within a system is an important operating characteristic that influences system performance. While the majority of previous studies on manufacturing performance consider product flows only as an implicit parameter of the design, we introduce an explicit measure of flow dominance based on entropy and test its efficacy in predicting the performance of manufacturing systems. In computing entropy flow dominance (EFD), we aggregate information embedded in the routings of all products within a system into a single measure. EFD is designed to indicate on a 0-1 scale the level of flow dominance, where 1 represents a pure flow shop and 0 represents a pure job shop. The result is a simple measure that provides managers a way to explain and predict complex phenomena. Our experimental results indicate that EFD is a statistically significant determinant of manufacturing system performance. Furthermore, the model including EFD as an independent variable accurately predicts manufacturing system performance as measured by job flow time, flow time standard deviation, and work in process. We note that the same results can also apply to service systems, such as the back-room low-contact type systems, that have similar characteristics as manufacturing systems.
机译：系统中的作业流是影响系统性能的重要操作特征。尽管先前有关制造性能的大多数研究都将产品流仅作为设计的隐含参数，但我们还是基于熵引入了显式的流动优势度量，并测试了其在预测制造系统性能方面的功效。在计算熵流优势（EFD）时，我们将嵌入系统中所有产品的路由中的信息汇总为一个度量。 EFD旨在以0-1的比例指示流程支配水平，其中1表示纯流程车间，0表示纯车间。结果是一种简单的方法，为管理人员提供了一种解释和预测复杂现象的方法。我们的实验结果表明，EFD是制造系统性能的统计显着决定因素。此外，包括EFD作为自变量的模型可以准确预测制造系统的性能，该性能可以通过工作流程时间，流程时间标准偏差和在制品进行衡量。我们注意到，相同的结果也可以应用于服务系统，例如与制造系统具有类似特征的后室低接触式系统。

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