Productivity is critical to the long-term competitiveness of factories. Therefore, the future productivity of factories must be estimated and enhanced. However, this is a challenging task because productivity can be improved based on a learning process that is highly uncertain. To address this problem, most existing methods fit fuzzy productivity learning processes and convert them into mathematical programming problems. However, such methods have several drawbacks, including the absence of feasible solutions, difficulty in determining a global optimum, and homogeneity in the solutions. In this study, to overcome these drawbacks, a specially designed artificial neural network (ANN) was constructed for fitting an uncertain productivity learning process. The proposed methodology was applied to an actual case of a dynamic random access memory factory. Experimental results showed that the ANN approach has a considerably higher forecasting accuracy compared with several existing methods.

中文翻译：

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使用人工神经网络方法拟合不确定的生产力学习过程

生产率对于工厂的长期竞争力至关重要。因此，必须估计并提高工厂的未来生产率。但是，这是一项具有挑战性的任务，因为可以基于高度不确定的学习过程来提高生产率。为了解决这个问题，大多数现有方法适合模糊生产力学习过程并将其转换为数学编程问题。但是，这样的方法有几个缺点，包括缺少可行的解决方案，难以确定全局最优值以及解决方案的同质性。在这项研究中，为了克服这些缺点，构建了一个特别设计的人工神经网络（ANN），以适应不确定的生产力学习过程。所提出的方法应用于动态随机存取存储器工厂的实际情况。