English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Forecasting the cycle time of each job is a critical task for a factory. However, recent studies have shown that it is a challenging task, even with state-of-the-art deep learning techniques. To address this challenge, a selectively fuzzified back propagation network (SFBPN) approach is proposed to estimate the range of a cycle time, the results of which provide valuable information for many managerial purposes. The SFBPN approach is distinct from existing methods, because the thresholds on both the hidden and output layers of a back propagation network are fuzzified to tighten the range of a cycle time, while most of the existing methods only fuzzify the threshold on the output node. In addition, a random search and local optimization algorithm is also proposed to derive the optimal values of the fuzzy thresholds. The proposed methodology is applied to a real case from the literature. The experimental results show that the proposed methodology improved the forecasting precision by up to 65%.

A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication