Most existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.

大多数现有的模糊协作预测（FCF）方法都采用类型1模糊数来表示模糊预测。基于区间值模糊数（IFN）的FCF方法并未得到广泛使用。但是，基于IFN的模糊预测的内部和外部提供了可用于不同管理目的的含义信息，这对于FCF方法而言是理想的功能。这项研究提出了一种基于IFN的FCF方法。与现有的基于IFN的模糊关联规则或模糊推理系统不同，基于IFN的FCF方法可确保所有实际值均在相应的模糊预测之内。此外，基于IFN的FCF方法分别利用聚合结果的内部和内部优化了预测精度和准确性。根据实验结果，