A Fuzzy Fluctuation Smoothing Rule for Job Dispatching in a Wafer Fabrication Factory: A Simulation Study

A Fuzzy Fluctuation Smoothing Rule for Job Dispatching in a Wafer Fabrication Factory: A Simulation Study

Toly Chen
Copyright: © 2012 |Pages: 17
DOI: 10.4018/ijfsa.2012100103
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Job dispatching in a wafer fabrication factory is a difficult task, mainly due to the complexity of the production system and the uncertainty involved in the production activities. Recently, a number of advanced dispatching rules were proposed, which estimate the remaining cycle times of jobs. This predictive nature is conducive to the effectiveness of these rules. If the uncertainty in the remaining cycle time can be better considered, incorrect scheduling will possibly be reduced. The tailored nonlinear fluctuation smoothing rule for mean cycle time (TNFSMCT) is fuzzified in this study, by expressing the remaining cycle time with a fuzzy value. The effectiveness of the proposed methodology is illustrated with a simulation study.
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A wafer fabrication factory is a very complicated manufacturing system. A job/lot in a wafer fabrication factory consists of 20~30 wafers that are handled or fabricated together. An operation refers to a wafer fabrication step. The process of a job contains all the steps it has to go through. In the basic wafer fabrication process, every job must go through hundreds of steps, and these steps are classified into basic categories (like photolithography, etching, stripping, etc.). As a result, a certain operation might be performed multiple times in the same job. In other words a job may have to visit the same workstation more than once for the same operation. For this reason a wafer fabrication factory is called a re-entrant production system. The cycle time to complete all operations for a job is usually up to several months, accompanied by the imbalance of capacity and batch production, which results in the accumulation of the work-in-progress (WIP). Consequently, reducing the cycle time is a critical task in managing a wafer fabrication factory (Ashok & Samuel, 2011), especially in a mass production setting such as in a memory fabrication factory. On the other hand, meeting due dates is of critical importance in low-volume as well as high-variety settings such as in the fabrication of application-specific integrated-circuits (ASIC) fabrication factory (Kim et al., 2001).

Kim et al. (2001) classified the scheduling problems in a wafer fabrication factory into three main categories: job release control, job scheduling for serial processing workstations, and batch scheduling for batch processing workstations. Job scheduling approaches can also be divided into global approaches and local approaches. Local approaches are usually focused on photolithography workstations (Wu et al., 2006), while global approaches can theoretically be applied to all types of workstations in a wafer fabrication factory.

Some studies (e.g., Berry & Rao, 1975; Lu et al., 1994; Wein, 1998) have shown that applying a traditional dispatching rule (such as first-in first out (FIFO), earliest due date (EDD), least slack (LS), shortest processing time (SPT), shortest remaining processing time (SRPT), critical ratio (CR), FIFO+, SRPT+, and SRPT++) to a wafer fabrication factory does not lead to very good results. Nevertheless, research in scheduling a wafer fabrication factory using a dispatching rule has become very important lately (Gupta & Sivakumar, 2006). In addition, in many wafer fabrication factory scheduling systems were installed more than five years ago. Although these systems are considered “satisfactory”, it is believed that they can be improved to provide more benefits, especially the scheduling/dispatching rules, testing environments, and the reporting tools (Pfund et al., 2008).

According to Chen (2009d), there are some problems facing the dispatching rules. First of all, not only the requirements of the jobs to be handled by the same machine but also the condition of the entire fabrication factory needs to be taken into consideration. Second, the dispatching rule must be tailored for the specific wafer fabrication factory. Third, the performance of a static dispatching rule is susceptible to changes in the production conditions, while a dynamic dispatching rule that incorporates stochastic variables will be more robust. Examples of dynamic dispatching rules include the fluctuation smoothing (FS) rules, the fluctuation smoothing policy for variance of cycle time (FSVCT) and the fluctuation smoothing policy for mean cycle time (FSMCT). However, these dynamic dispatching rules use the average values from the statistics for these stochastic variables, and as such are not very responsive to environmental changes. In addition, many dispatching rules are focused on a single performance measure, and minimizing any performance measure in such a complex job shop is strongly NP-hard. Nevertheless, optimizing multiple performance measures are still being pursued as well. In recent years, several advanced scheduling or dispatching algorithms have been proposed and some were successfully implemented in wafer fabrication factories as described below.

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