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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/702

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contributor.authorSudhakarapandian, R-
contributor.authorMahapatra, S S(Guide)-
date.accessioned2008-07-12T06:30:30Z-
date.available2008-07-12T06:30:30Z-
date.issued2007-
identifier.citationApplication of Soft Computing Techniques for Cell Formation Considering Operational Time and Sequence, thesis submitted in fullfilment of the award of the degree Doctor of Philosophy in Mechanical Engineeringen
identifier.urihttp://hdl.handle.net/2080/702-
descriptionCopyright for the published thesis belongs to NIT Rourkelaen
description.abstractIn response to demand in market place, discrete manufacturing firms need to adopt batch type manufacturing for incorporating continuous and rapid changes in manufacturing to gain edge over competitors. In addition, there is an increasing trend toward achieving higher level of integration between design and manufacturing functions in industries to make batch manufacturing more efficient and productive. In batch shop production environment, the cost of manufacturing is inversely proportional to batch size and the batch size determines the productivity. In real time environment, the batch size of the components is often small leading to frequent changeovers, larger machine idleness and so lesser productivity. To alleviate these problems, “Cellular Manufacturing Systems” (CMS) can be implemented to accommodate small batches without loosing much of production run time. Cellular manufacturing is an application of group technology (GT) in which similar parts are identified and grouped together to take advantage of their similarities in design and production. Similar parts are arranged into part families and each part family processes similar design and manufacturing characteristics. Cellular manufacturing is a good example of mixed model production and needs to resolve two tasks while implementing cellular manufacturing. The first task is to identify the part families and the next task is to cluster the production machines into machine cells known as cell formation (CF). GT ideas were first systematically presented by Burbidge following the pioneering work of Mitrofanov in U.S.S.R. Burbidge developed the concept of production flow analysis and successfully implemented in industries. After this, many countries started following GT concepts in their manufacturing lines. Researchers initiated to develop various methods like similarity coefficient method, graph theoretic approaches and array based methods in this field. In this trend, modeling of CMS through mathematical programming was started to incorporate more real life constraints on the problem. Later researchers started developing heuristics and meta-heuristics to explore the best optimal solutions for the CF problems. Since soft computing techniques nowadays expand their applications to various fields like telecommunications, networking, design and manufacturing, current research in CMS is being carried out using soft computing techniques. As for as representation of the cell formation problem is concerned, most of the researchers use zero-one binary machine part incidence matrix (MPIM) that is obtained from the route sheet of the manufacturing flow shop. The 1’s in the binary matrix represent the visit of the parts to the corresponding machines and 0’s represent the non-visit. The final output is a block diagonal structure from which the part families and corresponding machine cells where the part families are to be manufactured can be identified. In such an input representation, the process of clustering machines into machine cells and parts into part families is done without using real life information which may lead to inferior manufacturing plans. Therefore, there is a need to make use of as many as real life production information in the input matrix for representing the CF problem. In this research work, the real life production factors like, operational time of the parts in the machines known as workload data or ratio level data, operational sequence of the parts known as ordinal level data and batch size are considered for the problem representation. The methodology uses soft computing techniques like genetic algorithm (GA) and neural network to tackle the CF problem. In recent years, soft computing techniques have fascinated scientists and engineers all over the world because such techniques possess the ability to learn and recall as similar to the main functions of the human brain. They find better approaches to real world problems since soft computing incorporates human knowledge effectively. It deals with imprecision and uncertainty and learn to adapt to unknown or changing environment for better performance. In neural network, adaptive resonance theory (ART1) gives good results for binary MPIM CF problem. ART1 is not suitable for non-binary input pattern. Hence, in this work, suitable modification is included in the basic ART1 to incorporate the operational time of the parts, a ratio level non-binary data. For dealing with sequence of operations of the parts, an ordinal level non-binary data, a supplementary procedure is first implemented to convert the non-binary data into a suitable binary data and subsequently by feeding to the basic ART1 networks to solve the CF problem. Finally both operational time and operational sequence are combined and represented in a single matrix. The modified ART1 used for solving CF problem with operational time is applied to solve the problem with combination of operational time and sequence. The CF problem without any objective function is solved effectively by ART1 approach. For solving the CF problem with objective functions like total cell load variation (CLV) and exceptional elements, GA is proposed in this research work. CLV is calculated as the difference between the workload on the machine and the average load on the cell. Exceptional elements are the number of non-zero elements present in off diagonal blocks of the output matrix. Both the objective functions are combined to get a multi objective CF problem and solved by using GA. In the past, several performance measures like grouping efficiency and grouping efficacy have been proposed to find out the goodness of the output clusters. But most of them are applicable only for binary data representation. In this research work, suitable performance measures are proposed to measure the goodness of the block diagonal structure of the output matrix with ratio level data, ordinal level data and combination of both data. The algorithms are designed to handle problem of any size and they are coded with C++ and run on Pentium IV PC. Computational experience with the proposed techniques is presented and the results are compared with the problems available in open literature. The results are encouraging and the methodologies are found more appropriate for large scale production industries. Computational results suggest that the proposed approaches are reliable and efficient both in terms of quality and in speed in solving CF problems. Several directions for future studies are also addressed in this research.en
format.extent904328 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherNIT Rourkelaen
subjectCell Formationen
subjectAdaptive Resonance Theoryen
subjectGenetic Algorithmen
subjectCell load variationen
subjectExceptional Elementsen
subjectGrouping Efficiencyen
titleApplication of Soft Computing Techniques for Cell Formation Considering Operational Time and Sequenceen
typeThesisen
Appears in Collections:Thesis (Doctor of Philosophy)

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