A nonlinearly normalized back propagation network and cloud computing approach for determining cycle time allowance during wafer fabrication. (June 2017)

• Record Type: Journal Article
• Authors: Chen, Toly; Wang, Yi-Chi
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 45(2017)
• Page Start: 144
• Page End: 156
• Publication Date: 2017-06
• Subjects: Internal due date assignment -- Allowance determination -- Upper bound -- Wafer fabrication -- Back propagation network; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2015.11.005
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: This study investigated the determination of the allowance that must be added to the cycle time estimate, which is a critical concern when assigning internal due dates. Because no method for estimating cycle times is completely accurate, producing such estimates remains problematic but has rarely been addressed in the literature. A large allowance postpones the internal due date, diminishing company appeal when a factory manager negotiates with a customer. Therefore, in this study, a nonlinear approach was proposed to normalize the cycle times. After estimating the cycle time of a job by using a back propagation network, the allowance added to the cycle time can be effectively reduced through the collaboration of several computing clouds. Theoretical properties of the proposed method were validated, and a case from a wafer fabrication factory was used to evaluate the effectiveness of the proposed method in comparison with various existing methods. According to the experimental results, the proposed method facilitated establishing tight upper bounds on the cycle times. The proposed method was proven to be very effective. Highlights: A nonlinearly normalized BPN and cloud computing approach was proposed to establish tight upper bounds on the cycle time estimation. A case from a wafer fab was used to evaluate the effectiveness of the proposed method which was compared with various existing methods. The proposed methodology was improved by increasing the number of collaborating computing clouds. The proposed methodology was much more efficient under a cloud-computing environment than under other environments.