Analytic hierarchy process : An overview of applications

Co-authorship is one of the most tangible and well documented forms of scientific collaboration Almost every aspect of scientific collaboration networks can be reliably tracked by analysing co-authorship networks by bibliometric methods In the present study, scientific collaboration is considered both at individual and national levels, with special focus given to multinational collaborations Both literature data and original results witnessed a dramatic quantitative and structural change in the last decades of the 20th century The changes, to great extent, can be attributed to the universal tendencies of globalisation and the political restructuring of Europe The standards and, particularly, the visibility of scientific research, as a rule, benefit from the ever increasing level of collaboration, but the profits do not come automatically This fact underlines the necessity of a regular quantitative monitoring of inputs and outcomes, ie, bibliometric surveys

Analysing Scientific Networks Through Co-Authorship

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

Social networking on smartphones: When mobile phones become addictive

This paper begins to reconcile competing perspectives on the origins of competitive advantage by examining the adoption of ‘science-driven’ drug discovery, a performance-enhancing organizational practice. Science-driven drug discovery diffused slowly, allowing us to disentangle alternative theories of organizational heterogeneity. Adoption is driven by initial conditions, time-varying internal and external environmental conditions, and convergence (firms positioned least favorably adopt most aggressively). While accounting for initial conditions is critical, managers are sensitive to idiosyncratic environmental cues. The origins of competitive advantage may therefore lie in the ability to identify and respond to environmental cues well in advance of observing performance-oriented pay-offs. Copyright © 2000 John Wiley & Sons, Ltd.

/pdf/untangling-the-origins-of-competitive-advantage-44mmfa9aip.pdf

Untangling the Origins of Competitive Advantage

Neural networks are used in business organizations for providing intelligent decision support. The ABI/Inform bibliographic database has over 1 000 publications on business-related research in neur...

A bibliometric analysis

SUMMARY This paper presents a neural network clustering method for the part-machine grouping problem in group technology. Among the several neural networks, a Carpenter-Grossberg network is selected due to the fact that this clustering method utilizes binary-valued inputs and it can be trained without supervision. It is shown that this adaptive leader algorithm offers the capability of handling large, industry-size data sets due to the computational efficiency. The algorithm was tested on three data sets from prior literature, and solutions obtained were found to result in block diagonal forms. Some solutions were also found to be identical to solutions presented by others. Experiments on larger data sets, involving 10000 parts by 100 machine types, revealed that the method results in the identification of clusters with fast execution times. If a block diagonal structure existed in the input data, it was identified to a good degree of perfection. It was also found to be efficient with some imperfections i...

Machine-component cell formation in group technology : a neural network approach

The classification and coding of parts for group technology applications continue to be labour intensive and time-consuming processes. In this paper a pattern recognition approach utilizing neural networks is presented for the automation of some elements of this critical activity. As an illustrative example, a neural network system is used to generate part geometry-related digits of the Opitz code from bitmaps of part drawings. It is found to generate codes accurately and promises to be a useful tool for the automatic generation of shape-based classes and codes.

A neural network system for shape-based classification and coding of rotational parts

This study investigates the performance of Fuzzy ART neural network for grouping parts and machines, as part of the design of cellular manufacturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clustering experiments were performed, and the efficiency and consistency with which clusters were identified were examined, using large data sets of differing sizes and degrees of imperfection. The performance measures included the recovery ratio of bond energy and execution times, It is shown that Fuzzy ART neural network results in better and more consistent identification of block diagonal structures than ART1, a recent modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they were still superior to traditional algorithms for large data sets.

Performance of Fuzzy ART neural network for group technology cell formation

This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material flows in general. A pattern recognition approach based on artificial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be effectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental verification, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part-machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is effective in identifying good clustering solutions, consistently and with relatively fast execution times.

Shashidhar Kaparthi

Papers

A bibliometric analysis

Machine-component cell formation in group technology : a neural network approach

A neural network system for shape-based classification and coding of rotational parts

Performance of Fuzzy ART neural network for group technology cell formation

Sequence-dependent clustering of parts and machines: a Fuzzy ART neural network approach