Melody Y. Kiang

Dynamic grouping of parts in flexible manufacturing systems — a self-organizing neural networks approach

Abstract Artificial Intelligence (AI) has recently been recognized as a worthwhile tool for supporting manufacturing operations. This paper reviews AI-related approaches to Group Technology (GT) and presents the Self-Organizing Map (SOM) network, a special type of neural networks, as an intelligent tool for grouping parts and machines. SOM can learn from comples, multi-dimensional data and transform them into visually decipherable clusters. What sets this technique apart from others in GT is that SOM offers the flexibility of choosing from multiple grouping alternatives. SOM can be used in a dynamic situation where quick response to changes in part designs, process plans, or manufacturing conditions is essential, and thus it can be more easily integrated into a Flexible Manufacturing System. The paper proposes a framework of an intelligent system that integrates the neural networks approach and a knowledge-based system to provide decision supporting functions.

Self-organizing map network as an interactive clustering tool - An application to group technology

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to group technology. A computer program for implementing the SOM neural networks is developed and the results are compared with other clustering approaches used in group technology. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for part family formation in group technology.

Generalized case-based reasoning system for portfolio management

Abstract A case-based reasoning system (CBRS) is appropriate for an experince-rich domain, while a rule-based system performs reasonably well in a knowledge-rich application environment. Performance of a CBRS suffers when past experience is not readily available. A generalized case-based reasoning system (GCBRS) is proposed to remedy this weakness by incorporating domain theories represented as generalization rules. With these rules, previous experience (stored as cases) can be generalized so that the possibility of solving a new case is higher than it would be when case-based reasoning is used alone. The architecture and the inference mechanism of a GCBRS are discussed in this article. A portfolio management support system based upon the proposed GCBRS architecture is presented to demonstrate the feasibility of using GCBRS for developing a decision support system in a knowledge-poor and experience-poor domain. This article concludes with a discussion of future research.

Requirements for the design of a protocol suite to automate DSS deployment on the World Wide Web: A client/ server approach

Abstract The purpose of this paper is to propose and justify requirements for the design of a protocol suite for deploying and sharing Specific DSSs both within and across organizations by utilizing the World Wide Web (WWW) infrastructure and a client/server decomposition model. At the heart of the model proposed for the protocol suite is an approach for inter-agent communication as adapted from the distributed artificial intelligence literature. A modularized layered approach to protocol specification, and three sample client interfaces derived from the protocol are presented. Our approach is contrasted to alternative schemes for decision model access across wide area networks.

Qualitative reasoning in business, finance, and economics: introduction

Abstract This special volume of Decision Support Systems includes six papers on Qualitative Reasoning in Business, Finance, and Economics. These papers represent a growing interest in using the techniques of Qualitative Reasoning —an approach to formulating and solving physics and engineering problems which has undergone much recent development within the field of Artificial Intelligence — in intelligent decision support systems. Readers of this journal need no introduction to the issues in decision support systems in general, but a short introduction to the field of Qualitative Reasoning (QR) from the perspective of Artificial Intelligence is a useful prologue to the papers themselves. This introduction is necessarily the personal view of the editors, an addition to the many views of QR that have been published and debated elsewhere [2,4,5,7,15,16]

Improving the effectiveness of self-organizing map networks using a circular Kohonen layer

Kohonens self organizing map (SOM) network is one of the most important network architectures developed during the 1980s. The main function of SOM networks is to map the input data from an n dimensional space to a lower dimensional (usually one or two dimensional) plot while maintaining the original topological relations. A well known limitation of the Kohonen network is the boundary effect of nodes on or near the edge of the network. The boundary effect is responsible for retaining the undue influence of initial random weights assigned to the nodes of the network leading to ineffective topological representations. To overcome this limitation, we introduce and evaluate a modified, circular weight adjustment procedure. This procedure is applicable to a class of problems where the actual coordinates of the output map do not need to correspond to the original input topology. We tested the circular method with an example problem from the domain of group technology, typical of such a class of problems.

Reasoning in qualitatively defined systems using interval-based difference equations

Quantitative research and statistical techniques have long been regarded as superior ways of analyzing knowledge in social sciences. To deal with incomplete or imprecise knowledge while modeling systems, traditional approaches in social sciences (i.e., management science, operations research) have attempted to measure social facts by making approximations of the problem under analysis. Artificial intelligence (AI) research on qualitative reasoning which focuses on using qualitative knowledge to reason about the everyday physical world, suggests an opportunity to extend the capability of current logico-mathematical instruments used by social scientists. This paper proposes an interval propagation difference equation method, a type of qualitative-quantitative simulation method, to model dynamic systems by abstracting from the underlying true model. The proposed difference equation method can be used to model problems requiring discrete-time analysis, such as applications involving time-lag relationships. Moreover, the method does not require the exact functional form of the problem under analysis to be known with certainty. The incomplete or imprecise knowledge available about the functional form of the true model, and the values of its variables, are represented with bounding functions and interval values respectively. >

The Use of Qualitative and Causal Reasoning in Analytical Review Procedures

The use of analytic or quantitative models in auditing is still very limited and often informal. One of the more well developed areas in auditing is the use of models for analytical review. The objective of this research is to extend and apply the current qualitative and causal reasoning technology to accounting, especially analytical review procedures. Qualitative reasoning, initially developed and dedicated to physics, captures the structure, behaviors and the causality underlying the real model. It is believed that the introduction of qualitative and causal reasoning based on an understanding of the business environment can substantially improve intelligent support of current decision support systems.