Abstract
Productivity is always considered as one of the most basic and important factors to the competitiveness of a factory. For this reason, all factories have sought to enhance productivity. To achieve this goal, we first need to estimate the productivity. However, there is considerable degree of uncertainty in productivity. For this reason, a fuzzy collaborative forecasting approach is proposed in this study to forecast the productivity of a factory. First, a learning model is established to estimate the future productivity. Subsequently, the learning model is converted into three equivalent nonlinear programming problems to be solved from various viewpoints. The fuzzy productivity forecasts by different experts may not be equal and should therefore be aggregated. To this end, the fuzzy intersection and back propagation network approach is applied. The practical example of a dynamic random access memory (DRAM) factory is used to evaluate the effectiveness of the proposed methodology.
Highlights
Seeing a problem from various perspectives ensures that no parts are ignored when solving the problem
A practical example from a dynamic random access memory (DRAM) factory is used to evaluate the effectiveness of the fuzzy collaborative forecasting approach (Table 3)
All nine fuzzy linear regression equations were applied to forecast the productivity of the DRAM factory
Summary
Seeing a problem from various perspectives ensures that no parts are ignored when solving the problem. A fuzzy collaborative forecasting approach is proposed in this study to forecast the productivity of a factory, which is important because of the following reasons [4]. The long-term outputs are affected by price fluctuations and are very difficult to estimate To solve this problem, this study adopts the following method. Productivity is affected by many factors including standardizing, quality differences, scrap rates, new workers, layoffs, and incentive plans [4], and there is a considerable degree of uncertainty in the long-term productivity To consider this uncertainty, this study uses a fuzzy value to represent each forecast of productivity. Each expert applies a fuzzy linear regression equation to forecast the productivity of a factory. The forecasted value should be as close as possible to the actual value; Table 1: The number of adjustable parameters in various methods
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