Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties
Characterizations and Optimization for Resilient Manufacturing Systems With Considerations of Process Uncertainties
复制标题
考虑过程不确定性的弹性制造系统的表征和优化
DOI：
10.1115/1.4055425
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发表时间：
2023
影响因子：
3.1
通讯作者：
Wang, Zimo
中科院分区：
文献类型：
--
作者：
Ma, Qiyang;Che, Yiming;Cheng, Changqing;Wang, Zimo
最近的COVID-19大流行揭示了全球供应链的脆弱性：不可预见的供应紧缩和客户需求的不可预测变化导致生产计划和管理的灾难性中断，导致大多数制造系统的生产力大幅波动。因此，一个智能和弹性的制造系统（S&RMS）有望承受这种意外的扰动，并迅速调整，以减轻其对系统稳定性的影响。然而，建模系统的破坏性事件的影响的弹性还没有得到充分解决。研究了一种基于广义多项式混沌（gPC）展开的离散事件动态系统（DEDS）模型，用于捕捉制造系统中的不确定性和不规则破坏性事件。该分析方法允许对生产计划进行实时优化，以减轻间歇性破坏性事件（例如，供应短缺），并增强系统的弹性。混合轴承制造车间的案例研究表明，所提出的方法允许及时干预生产计划，以显着减少停机时间（约五分之一的停机时间相比，一个没有控制），同时保证最大的生产率下的系统扰动和不确定性。
The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system’s stability. However, modeling the system’s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system’s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties.