摘要:

This paper examines the design and implementation of a knowledge-based decision support system (KBDSS) in the facility location domain. The KBDSS for locating a manufacturing facility is intended to support facilities planners in improving the quality of strategic decision-making in facility location. A survey of past location studies was conducted to identify the major considerations of location analysts and to develop a hierarchy of factors for locating a manufacturing facility in the USA. Location evaluation is accomplished via a location model that is formulated as a linear additive multiattribute utility model. The microcomputer-based DSS developed integrates an object-oriented expert system with other modeling tools, such as a database management system and a C-based interactive graphical user interface. The system incorporates subjective, judgemental evaluations from management with objective, factual location data to recommend the best location for a new manufacturing facility. The system also makes transparent the justification for its recommendations by providing appropriate textual and graphical explanation facilities.

ISSN:0740-817X    

DOI:10.1080/07408179508936796

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

A review of the literature on control charts for multivariate quality control (MQC) is given, with a concentration on developments occurring since the mid-1980s. Multivariate cumulative sum (CUSUM) control procedures and a multivariate exponentially weighted moving average (EWMA) control chart are reviewed and recommendations are made regarding their use. Several recent articles that give methods for interpreting an out-of-control signal on a multivariate control chart are analyzed and discussed. Other topics such as the use of principal components and regression adjustment of variables in MQC, as well as frequently used approximations in MQC, are discussed.

ISSN:0740-817X    

DOI:10.1080/07408179508936797

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

摘要:

Quality improvement efforts often make use of various mathematical models that describe the relationships between quality characteristics and process factors. Such models typically come from a variety of sources: experiments, theory, on-line data analysis, expertise, and other process documents. These sources of knowledge are often distinct and separate, often yielding models with slightly different predictions, having different precision and validity. In this paper we explore alternatives in which different mathematical models can be integrated together into a single prediction that takes into account both model validity and model variability. Some guidelines for establishing and quantifying model validity are presented. The approach is demonstrated within the context: of predicting surface finish in a machining process.

ISSN:0740-817X    

DOI:10.1080/07408179508936798

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0740-817X    

DOI:10.1080/07408179508936799

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0740-817X    

DOI:10.1080/07408179508936800

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor

ISSN:0740-817X    

DOI:10.1080/07408179508936801

出版商:Taylor & Francis Group    

年代:1995

数据来源: Taylor