Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data created great opportunity for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed...

Recent developments in modeling stream of variation in multistage manufacturing system along with the urgent need for yield enhancement in the semiconductor industry has led to complex large scale simulation problems in design and performance prediction, thus challenging current Monte Carlo (MC) based simulation techniques. MC method prevails in statistical simulation approaches for multi-dimen...

In many manufacturing processes, process data are observed in the form of time-based profiles, which may contain rich information for process monitoring and fault diagnosis. Most approaches currently available in profile monitoring focus on single-stage processes or multistage processes with repeating cyclic profiles. However, a number of manufacturing operations are performed in multiple stage...

A new engineering-driven factor analysis (EDFA) method has been developed to assist the variation source identification for multistage manufacturing processes (MMPs). The proposed method investigated how to fully utilize qualitative engineering knowledge of the spatial variation patterns to guide the factor rotation. It is shown that ideal identification can be achieved by matching the rotated ...

This paper analyzed the model design process of data warehouse project and the various factors which affected the performance in data warehouse model design. Then the author introduced the multistage fuzzy comprehensive evaluation method, and studied its application to the data warehouse’s model performance evaluation, and gave the performance measure model of data warehouse based on multistage...

We prove the almost-sure convergence of a class of samplingbased nested decomposition algorithms for multistage stochastic convex programs in which the stage costs are general convex functions of the decisions, and uncertainty is modelled by a scenario tree. As special cases, our results imply the almost-sure convergence of SDDP, CUPPS and DOASA when applied to problems with general convex cost...

A multistage decision process for transportation investment planning is described. Most transportation investment problems inherently involve multiple criteria and uncertainty. To deal with the variations arising from changes in external environments, robust contingency plans should be integrated in the planning process-not after plans are developed-to account for uncertainty. With the proposed...

The aim of the paper is to address the issue of operational improvements in manufacturing firms, especially when yield losses result in a significant increase of the production cost. In a multistage production environment the firm must decide which production stage(s) should receive more “attention” in order to minimize the overall cost incurred from yield losses. Yield variability is a factor ...

In this chapter we discuss a tactical optimisation problem that arises in a multistage distribution system where customer orders can be delivered from any stockpoint. A simple rule to allocate orders to locations is a break quantity rule, which routes large orders to higher-stage stockpoints and small orders to end-stockpoints. A so-called break quantity determines whether an order is small or ...

Because applying machine learning techniques in support of clinical decision would improve decision makers in healthcare, we present in this paper a comparative framework of Support Vector Machine (SVM) classifiers based on post operative patient (POP) data. We compare the performance of a single multiclass SVM and a multistage SVM (MSVM) to those obtained by a number of other classifiers prese...