A Fuzzy Collaborative Forecasting Approach for Forecasting the Productivity of a Factory

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.

Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach

“…In existing fuzzy forecasting methods, a BPN is usually constructed to defuzzify the aggregation result with the following configuration, [13,40]:…”

“…(3) Output: the forecast. (4) The training algorithm: The gradient descent (GD) algorithm and the Levenberg-Marquardt (LM) algorithm are two prevalent training algorithms for this purpose [8,40]. (5) Convergence criteria: The training process stops when the sum of squared error (SSE) falls below a prespecified threshold,…”

“…(3) Output: o j , to be compared with y j . (4) The training algorithm: the GD algorithm to prevent overfitting [40]. (5) Convergence criteria: The training process stops when SSE falls below 10 -4 or a maximal number of 1000 epochs have been run.…”

A Fuzzy Collaborative Approach for Evaluating the Suitability of a Smart Health Practice

“…If the possible values of α are enumerated, e.g., every 0.1, then Equations (8) and (9) need to be solved 10• 2 C n 2 + 1 times, from which the minimal and maximal results specify the lower and upper bounds of the α cut [29][30][31]:…”

An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach

“…shows how the BPN defuzzifier is incorporated in the proposed methodology. Training algorithm: A gradient descent (GD) algorithm is used to prevent overfitting[45]. (6) Convergence criteria: Training terminates when the sum of squared error,…”