The measurement of complexity in production and other commercial systems
The measurement of complexity in production and other commercial systems
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生产和其他商业系统复杂性的测量
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Yu. M. Suhov
中科院分区：
文献类型：
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作者：
G. Frizelle;Yu. M. Suhov
本文回顾了制造业和商业工业中生产系统复杂性的三个案例研究，并在这些研究的基础上发展了数学方法。我们使用在考虑中的系统分析中自然出现的各种（长期）熵率作为复杂性的度量；在我们的示例中，主要关注（物理或虚拟）队列和相关现象。因此，当一个系统的熵率较高时，它被认为是“更复杂”的。当一个给定系统的不同子系统相互比较，确定一个“瓶颈”时，同样的原则也适用。熵率的数值是在观测和记录过程中根据一些简化的假设确定的。为了使我们能够进行有效的比较，我们在所研究的系统中引入了与队列相关的条件的各种分类。我们还讨论了这里出现的一些实际问题，包括噪声和数据丢失。
The paper gives a review of three case studies of complexity of production systems in manufacturing and commercial industry and develops mathematical methods stemming from these studies. We use as measures of complexity various (long term) entropy rates that naturally emerge in the analysis of systems under consideration; in our case, the main focus is on (physical or virtual) queues and related phenomena. Consequently, a system is considered ‘more complex’ when its entropy rates are higher. The same principle is applied when different subsystems of a given system are compared with each other, identifying a ‘bottleneck’. The numerical values for entropy rates are determined in the course of observation and recording, subject to some simplifying assumptions. To enable us to make effective comparisons, we introduce various classifications of queue-related conditions in systems under investigation. We also discuss a number of practical issues that emerge here, including noise and data loss.