Sitanath Mazumdar

Application of visual clustering properties of self organizing map in machine-part cell formation

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, work-in-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using self organizing map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.

Neuro-genetic impact on cell formation methods of Cellular Manufacturing System design: A quantitative review and analysis

This paper presents a quantitative review of the influence and the impact of the two major soft computing approaches, Artificial Neural Network and Genetic Algorithm on cell formation methods of the design of Cellular Manufacturing System (CMS). An in-depth analysis has been carried out to identify the research trend, for the last two decades that captures the chronological progress and continuous improvement in the design of CMS. The in-depth quantitative analysis helped to identify the trend of research, improvements over the years and the capability of the soft-computing approaches to handle complex data-sets with different objective functions. The comparative study of the computational time, number of cells formed and the clustering efficiency obtained, helped to figure out the success rates of each approach and the progress achieved since early 1990s till recent times.

PRINCIPAL COMPONENT ANALYSIS AND SELF-ORGANIZING MAP FOR VISUAL CLUSTERING OF MACHINE-PART CELL FORMATION IN CELLULAR MANUFACTURING SYSTEM

The present paper attempts to generate visual clustering and data extraction of cell formation problem using both principal component analysis (PCA) and self-organizing map (SOM) from input of sequence based on the machine-part incidence matrix. Firstly, the focus is to utilize PCA for extracting high-dimensionality of input variables and project the dataset onto a 2D space. Secondly, the unsupervised competitive learning of SOM algorithm is used for data visualization and subsequently, to solve cell formation problem based on ordinal sequence data via the node cluster on the SOM map. Although the numerically illustrated results from dataset revealed that PCA has explained most of the cumulative variance of data but in reality, when the very large-dimensional cell formation problem based on sequence is available then, obtaining the clustering structure from PCA projection becomes very difficult. Most importantly, in the visual clustering of ordinal data, the use of U-matrix alone cannot be efficient to get the cluster structure but with color extraction, hit map, labeling via the SOM node map it becomes a powerful clustering visualization methodology and thus, the present research contribute significantly in the research of cellular manufacturing.

Growing Hierarchical Self-Organizing Map Computation for Clustering in Cellular Manufacturing

This article focuses on approach that provides visualization of machine–part clustering in cellular manufacturing system based on sequence of operation. We propose a novel cell formation approach, namely the growing hierarchical self-organizing map (GHSOM), for dealing with 14 benchmark problems from literature. The performance of the proposed algorithm is tested with the problem data sets and the results are compared using the group technology efficiency (GTE) and computational time with the existing traditional clustering algorithms. It is found that the proposed algorithm resulted in an increase in GTE in most of the problem data sets, and the outputs of cell formation are either superior or same as existing methods. The outputs of the experiments conducted in this research lead us to the conclusion that the GHSOM is a promising alternative cell formation algorithm owing to its adaptive architecture and the ability to expose the hierarchical structure of data.

Market mediated authenticity in culturally themed culinary space

Purpose This paper explores authenticity as a multidimensional construct from both consumer and service provider perspective in the context of culturally themed restaurants in Kolkata, India. Design/methodology/approach Utilizing a phenomenological design, data were collected through participant observation, photographs, and semi-structured interviews in Bengali themed restaurants over a two-year period. Findings By articulating the processes and dimensions that operate behind the narrative of authenticity, the findings display the interaction between market/cultural forces and the perception of authenticity. They reveal that authenticity embraces four major categories including traditional, staged-form, postmodern, and constructivist. Research limitations/implications The study provides insights into the collective role of both consumers and service providers in mediating perceptions of authenticity. Theoretically, the study contributes to the literature by articulating four dimensions of authenticity, P...

Socio-Economic Profile of Shopkeepers in Embedded Markets: Social and Cultural Perspectives From a Hundred Years of Bengali Literary Works

ABSTRACT Retailing, in the context of developing economies, has been claimed to be a social practice. A review of fictional literature from the ethno-linguistic region of Bengal may enhance our understanding of the social and cultural history of independent retailing. The evidence from social sciences shows that most retail markets in the economically less developed countries function in similar ways. Such a similarity in social structures may be explained using the concept of embeddedness. This article identifies the history of socio-economic roles of rural unorganised retailers in the embedded markets of developing economies through the discourse analysis of Bengali fictional literature.

From Local Taste to Luxury Experience: Insights into Culinary Distinction

Luxury has always held an element of fascination for consumers across the globe. Luxury exists in all facets of life: in cosmetics, watches, jewellery, fragrances, automobiles, fashion, and hospitality, and is established through the processes of linking of one’s self-image, their creative self-identity, and their personal tastes as consumers. Yet what conveys the image and makes it luxurious is not the product; rather, it is the brand, and the experience of it, that evokes the sensation of luxury. Consumers acknowledge brands such as TAG Heuer, Prada or BMW beyond their craftsmanship or technical superiority, and connect with them through their emotional engagement with the image, implying that the age-old definition of luxury based on functionality or craftsmanship has been taken over by a newer definition that highlights the brand’s dominance over its clients (Kapferer and Bastien 2009a). The meaning of luxury has shifted over the years from commodities (rare pearls, crystal, perfumes, and spices) in the 17th century, to craftsmanship, superior quality and customer service (Berthon et al. 2009) in the 19th century, and now to that of branding, where luxury is judged on the basis of its pleasure components (Atwal and Williams 2009).