DataSheet1_Threshold-impeded stochastic production: how noise interacts with disruptive thresholds to affect the production output in fluctuating environments.CSV

Introduction: Production systems are bound to operate in stochastic conditions. Prominent sources of performance-reducing uncertainty are constituted by machine failures, decision errors, and fluctuating supplies. This article offers a novel approach to uncertainty through modelling and simulation of nonlinear production systems. In particular, the authors consider production systems where the output is drastically reduced when a resource of fluctuating input values reaches an upper threshold.
Methods: The article introduces minimal models of such hreshold-impeded stochastic production (TISP) systems and the system performance (i.e., the output) is analyzed as a function of system parameters (e.g., the type of nonlinearity) and noise input features (e.g., the distribution width or time correlations). Applications to steel manufacturing via continuous casting and power generation through wind turbines are discussed in detail.
Results and Discussion: The simulation experiments illustrate that especially the extent of the input fluctuations affects the output performance which is why the authors recommend that TISP system operators counterbalance such fluctuations if possible.

引言：生产系统必然在随机工况下运行。导致系统性能下降的不确定性主要源自设备故障、决策失误与供给波动。本文通过对非线性生产系统的建模与仿真，为应对此类不确定性提供了一种新颖的研究思路。具体而言，本文所研究的生产系统满足：当输入值存在波动的资源达到上限阈值时，系统产出会出现大幅下滑。
方法：本文提出了此类阈值约束型随机生产（threshold-impeded stochastic production, TISP）系统的极简模型，并以系统参数（如非线性类型）与噪声输入特性（如分布宽度或时间相关性）为自变量，对系统性能（即产出量）展开分析。此外，本文还详细探讨了该模型在连铸钢铁制造与风力涡轮机发电场景中的具体应用。
结果与讨论：仿真实验结果表明，输入波动的幅度对系统产出性能影响尤为显著，据此本文建议TISP系统的运营者在条件允许的前提下，对这类输入波动进行补偿调节。