Abstract
A fuzzy dynamic-prioritization agent-based system was developed in this study to improve the forecasting of the cycle time of a job in a wafer fabrication plant (wafer fab). In this system, multiple fuzzy agents forecast the cycle time of a job from various viewpoints, after which the aggregation and evaluation agent aggregates these fuzzy cycle time forecasts using an innovative operator (i.e., the fuzzy weighted intersection) into a single representative value. Subsequently, the optimization agent varies the authority levels of the fuzzy cycle time forecasting agents to optimize the forecasting performance. A practical example was used to evaluate the effectiveness of the fuzzy dynamic-prioritization agent-based system. The experiment results indicated that the fuzzy dynamic-prioritization agent-based system outperformed three rival methods in improving forecasting accuracy. In addition, the forecasting performance could be enhanced by discriminating the authority levels of the fuzzy cycle time forecasting agents.
Highlights
The cycle time, or manufacturing lead time, of a job is the time it takes for the job to go through a factory [25, 59]
Step 2 Each fuzzy cycle time forecasting agent constructs and trains an fuzzy backpropagation networks (FBPNs) to forecast the cycle time of a job based on the received data
Each agent constructs an FBPN to forecast the cycle time of a job according to the values of some production conditions collected when the job is released into a factory [9]
Summary
The cycle time, or manufacturing lead time, of a job is the time it takes for the job to go through a factory [25, 59]. Some forecasting methods are more suitable than others for a specific forecasting task To overcome these problems, this study proposed a fuzzy dynamic-prioritization agent-based system for forecasting the job cycle time in a wafer fabrication plant (wafer fab). In this proposed system, multiple fuzzy agents construct fuzzy backpropagation networks (FBPNs) or fuzzy deep neural networks (FDNNs) to collaboratively forecast the cycle time of a job. Wang et al [54, 55] constructed a density peak-based radial basis function network (RBFN) to forecast the cycle time of a job They classified jobs before forecasting the cycle times, which was common in previous studies [10, 14]. Wang et al [54] focused on identifying
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