A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Abstract

Abstract. Estimating the cycle time of each job in a wafer fabrication factory is critical.An equally critical problem is to estimate the range of a cycle time. This topic has rarely been discussed because most existing methods for range calculation establish only a symmetric range. However, a symmetric range does not meet the requirements of managerial applications based on the lower and upper bounds of a cycle time. Recently, a few researchers have attempted to establish the asymmetric bounds of a cycle time.However, these methods either have overly complex computations or do not consistently perform well. This study proposes a new approach for effective cycle-time bounding.First, construction of a back propagation network predicts the cycle time of a job. Second, two linear functions of the output are formed, and the threshold on the output node is fuzzified to derive the lower and upper bounds of the cycle time. In theory, such a treatment tightens the lower and upper bounds and improves the forecasting precision.Third, theorems for the feasibility of the two linear functions are proved. Fourth, a cloud computing scheme is proposed to improve the bounds in an effective manner. Finally, a real case illustrates the applicability of the proposed methodology. Experimental results show that this methodology narrows the range of cycle times for untrained data by 31%, while maintaining a considerably high hit rate of 92.5%.2

Fuzzy dynamic-prioritization agent-based system for forecasting job cycle time in a wafer fabrication plant

“…Wang et al [54] focused on identifying factors critical for forecasting the cycle time of a job. To this end, correlation analyses [16] have been conducted. Subsequently, they fitted an adaptive logistic regression equation to generate a job cycle time forecast.…”

“…Chen and Wu [16] constructed an FBPN to generate a fuzzy cycle time forecast. The FBPN was efficient because only the threshold of the output node was fuzzified.…”

“…Each agent constructs an FBPN (or FDNN) 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]. Such production conditions include job size, factory utilization, queue length on the processing route, bottleneck queue length, factory queue length, factory work in process, average lateness, future workload, and forecasting error [8,16,42]. In the literature, various techniques for select the relevant production conditions have been employed, such as backward-elimination-based regression analysis [7], backward-elimination-based genetic programming [4], conditional mutual-information-based feature selection [52], and adaptive logistic regression correlation analysis [54,55].…”

“…In particular, the spread of a fuzzy cycle time forecast provides information about the range of the cycle time [7]. However, such forecasting is based on the prerequisite that a fuzzy cycle time forecast contains actual values [16]. To ensure this, fuzzy collaborative forecasting methods have been proposed to narrow the range of a cycle time forecast through the collaboration of multiple experts (or agents, [11,13,16].…”

An Approach for Multipath Cloud Manufacturing Services Dynamic Composition

“…Driven by market demands, the manufacturing industry is undergoing significant transformations. With the rapid development of information technology, such as web service, 1-4 cloud computing, [5][6][7][8] manufacturing technology, 9 manufacturing grid, 10 Internet of things, 11,12 and other networked manufacturing modes, cloud manufacturing (CMfg) is proposed and developed successfully. [13][14][15] The main purpose of CMfg is to provide user with on-demand, always-ready, high-quality and low-consumption service, which is available for product design, manufacturing, testing, simulation and maintenance, and other manufacturing lifecycle process.…”