Forecasting future productivity is a critical task to every organization. However, the existing methods for productivity forecasting have two problems. First, the logarithmic or log-sigmoid value, rather than the original value, of productivity is dealt with. Second, the objective functions are not consistent with those adopted in practice. To address these problems, a fuzzy polynomial fitting and mathematical programming (FPF-MP) approach are proposed in this study. The FPF-MP approach solves two polynomial programming problems, based on the original value of productivity, in two steps to optimize accuracy and precision of forecasting future productivity, respectively. A real case was adopted to validate the effectiveness of the proposed methodology. According to the experimental results, the proposed FPF-MP approach outperformed six existing methods in improving the forecasting accuracy and precision.

中文翻译：

---

模糊多项式拟合和数学规划方法可提高生产率预测的准确性和准确性

预测未来的生产率是每个组织的关键任务。但是，现有的生产率预测方法存在两个问题。首先，处理生产率的对数或对数S形值，而不是原始值。其次，目标功能与实践中采用的功能不一致。为了解决这些问题，本研究提出了一种模糊多项式拟合和数学规划（FPF-MP）方法。FPF-MP方法基于生产率的原始值，分两个步骤分别解决了两个多项式编程问题，以分别优化预测未来生产率的准确性和精度。通过了一个实际案例来验证所提出方法的有效性。根据实验结果，