Identification of out of control quality characteristics in a multivariate manufacturing environment

There are many instances in which the quality of a product or constancy of a process is determined by the joint levels of several attributes or properties. During the conduct of such a process or the production of such a product, one wishes to detect as quickly as possible any departure from a satisfactory state, while at the same time identifying which attributes are responsible for the deviation. In most cases of practical interest, however, there exist correlations among the several properties of interest; this makes it advisable to monitor certain aggregate characteristics of the process, rather than observing its various components separately. When the mean vector of the quality attributes is the major concern, this aggregate monitoring function is most commonly implemented via a T ² chart. The dependencies among attributes, however, complicate the determination of which are responsible when a deviation occurs. This paper presents an approach to help identify aberrant variables when Shewhart type multivariate control charts based on Hotelling's T ² are in use.     

The problems of Indian history

Most historians write Indian history from a Euro-American perspective. Two great historians illustrate the problem. Francis Parkman wrote the story of the Euro-Americans' victory over the Indians. Bernard Bailyn describes the social process of settlement neutrally, but ignores the Indians. So long as scholars define the American past in this way, Indian history has no significance on its own terms. If they focus only on “intersections” of Europeans and native “obstacles,” Indians are merely symbols. The many problems fall into three categories: structural, methodological, and conceptual.     

Fuzzy vs. Minmax Weighted Multiobjective Linear Programming Illustrative Comparisons

This paper compares two approaches to the solution of weighted multiobjective linear programming problems: the fuzzy linear programming method and the minmax distance metric. The two models produce an identical solution for equally weighted objectives, but the solutions differ when the objectives are unequally weighted. This is due to the underlying meaning of the weights attached to each solution method. The paper illustrates the graphical meaning of the weights and the implications to the decision maker.