Randomness Modeling in Supply Chain Simulation

Galina Merkuryeva, O. Vecherinska
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引用次数: 5

Abstract

Stochastic simulation models utilize probability distributions to represent a multitude of randomly occurring events. Theoretical distributions are commonly used to model the randomness of a real process because they help to smooth data irregularities that may exist due to the values missed during the data collection phase. These distributions can be selected either by fitting a distribution to the data collected, or based on the known properties of the process being modelled. The incompatibility between specific characteristics of the theoretical distribution and assumptions of simulation and mathematical calculus present an actual problem in supply chains. The paper is based on the analysis of mentioned contradictions. Different approaches to deal with theoretical probability distributions in supply chains are described in the paper.
供应链仿真中的随机建模
随机模拟模型利用概率分布来表示大量随机发生的事件。理论分布通常用于模拟真实过程的随机性,因为它们有助于平滑由于数据收集阶段丢失的值而可能存在的数据不规则性。这些分布可以通过将分布拟合到所收集的数据中来选择,也可以基于正在建模的过程的已知属性来选择。理论分布的具体特征与模拟和数学演算假设之间的不相容是供应链中的一个实际问题。本文就是在对上述矛盾进行分析的基础上展开的。本文描述了处理供应链理论概率分布的不同方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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