## Application of Soft Computing Techniques for Cell Formation Considering Operational Time and Sequence

Material type: TextLanguage: English Publisher: 2007Description: 186 pSubject(s): Engineering and Technology | Mechanical Engineering | Mechanical EngineeringOnline resources: Click here to access online Dissertation note: Thesis (Ph.D)- National Institute of Technology, Rourkela Summary: In response to demand in market place, discrete manufact uring firms need to adopt batch type manufacturing for incorporating con tinuous and rapid changes in manufacturing to gain edge over competitors. In addition, there is an increasing trend toward achieving higher level of integ ration between design and manufacturing functions in industries to make batch manuf acturing more efficient and productive. In batch shop production environment, the cost of manufacturing is inversely proportional to batch size and the batch si ze determines the productivity. In real time environment, the batch size of the components is often small leading to frequent changeovers, larger machine i dleness and so lesser productivity. To alleviate these problems, “Cellular M anufacturing Systems” (CMS) can be implemented to accommodate small batches wi thout loosing much of production run time. Cellular manufacturing is an application of group technology (GT) in which similar parts are identified a nd grouped together to take advantage of their similarities in design and productio n. Similar parts are arranged into part families and each part family proce sses similar design and manufacturing characteristics. Cellular manufacturing i s a good example of mixed model production and needs to resolve two tasks wh ile implementing cellular manufacturing. The first task is to identify the part families and the next task is to cluster the production machines into machine cell s known as cell formation (CF). GT ideas were first systematically prese nted by Burbidge following the pioneering work of Mitrofanov in U.S.S .R. Burbidge developed the concept of production flow analysis and successfully implem ented in industries. After this, many countries started following GT concepts in their manufacturing lines. Researchers initiated to develop various methods l ike similarity coefficient method, graph theoretic approaches and array based meth ods in this field. In this trend, modeling of CMS through mathematical programm ing 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 nowa days expand their applications to various fields like telecommunications, net working, design and ii 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 inci dence matrix (MPIM) that is obtained from the route sheet of the manufact uring 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 w here the part families are to be manufactured can be identified. In such an in put representation, the process of clustering machines into machine cells and parts in to part families is done without using real life information which may le ad to inferior manufacturing plans. Therefore, there is a need to make use of as m any as real life production information in the input matrix for representing th e 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 ra tio level data, operational sequence of the parts known as ordinal le vel data and batch size are considered for the problem representation. The methodo logy uses soft computing techniques like genetic algorithm (GA) and n eural network to tackle the CF problem. In recent years, soft computing techniq ues have fascinated scientists and engineers all over the world because such te chniques possess the ability to learn and recall as similar to the main fun ctions of the human brain. They find better approaches to real world problems since soft computing incorporates human knowledge effectively. It deals with i mprecision and uncertainty and learn to adapt to unknown or changing environment for better performance. In neural network, adaptive resonance the ory (ART1) gives good results for binary MPIM CF problem. ART1 is not suitabl e for non-binary input pattern. Hence, in this work, suitable modification i s included in the basic ART1 to incorporate the operational time of the parts, a r atio level non-binary data. For dealing with sequence of operations of the parts, an or dinal 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 operati onal time and operational iii sequence are combined and represented in a single matrix . The modified ART1 used for solving CF problem with operational time is a pplied to solve the problem with combination of operational time and sequence. T he CF problem without any objective function is solved effectively by ART1 appro ach. For solving the CF problem with objective functions like total cell load variation (CLV) and exceptional elements, GA is propose d 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 m atrix. Both the objective functions are combined to get a multi objective CF prob lem and solved by using GA. In the past, several performance measures like group ing efficiency and grouping efficacy have been proposed to find out the g oodness of the output clusters. But most of them are applicable only for binar y data representation. In this research work, suitable performance measures are propo sed to measure the goodness of the block diagonal structure of the output ma trix 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 techniq ues 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 fut ure studies are also addressed in this research.Item type | Current location | Collection | Call number | Status | Date due | Barcode |
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Thesis (Ph.D/M.Tech R) | BP Central Library Thesis Section | Reference | Not for loan | T28 |

Thesis (Ph.D)- National Institute of Technology, Rourkela

In response to demand in market place, discrete manufact

uring firms need

to adopt batch type manufacturing for incorporating con

tinuous and rapid

changes in manufacturing to gain edge over competitors.

In addition, there is an

increasing trend toward achieving higher level of integ

ration between design and

manufacturing functions in industries to make batch manuf

acturing more efficient

and productive. In batch shop production environment,

the cost of manufacturing

is inversely proportional to batch size and the batch si

ze determines the

productivity. In real time environment, the batch size

of the components is often

small leading to frequent changeovers, larger machine i

dleness and so lesser

productivity. To alleviate these problems, “Cellular M

anufacturing Systems”

(CMS) can be implemented to accommodate small batches wi

thout loosing much

of production run time. Cellular manufacturing is an

application of group

technology (GT) in which similar parts are identified a

nd grouped together to take

advantage of their similarities in design and productio

n. Similar parts are

arranged into part families and each part family proce

sses similar design and

manufacturing characteristics. Cellular manufacturing i

s a good example of

mixed model production and needs to resolve two tasks wh

ile implementing

cellular manufacturing. The first task is to identify the

part families and the next

task is to cluster the production machines into machine cell

s known as cell

formation (CF). GT ideas were first systematically prese

nted by Burbidge

following the pioneering work of Mitrofanov in U.S.S

.R. Burbidge developed the

concept of production flow analysis and successfully implem

ented in industries.

After this, many countries started following GT concepts

in their manufacturing

lines. Researchers initiated to develop various methods l

ike similarity coefficient

method, graph theoretic approaches and array based meth

ods in this field. In this

trend, modeling of CMS through mathematical programm

ing 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 nowa

days expand their

applications to various fields like telecommunications, net

working, design and

ii

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 inci

dence matrix (MPIM)

that is obtained from the route sheet of the manufact

uring 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 w

here the part families

are to be manufactured can be identified. In such an in

put representation, the

process of clustering machines into machine cells and parts in

to part families is

done without using real life information which may le

ad to inferior manufacturing

plans. Therefore, there is a need to make use of as m

any as real life production

information in the input matrix for representing th

e 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 ra

tio level data,

operational sequence of the parts known as ordinal le

vel data and batch size are

considered for the problem representation. The methodo

logy uses soft

computing techniques like genetic algorithm (GA) and n

eural network to tackle

the CF problem. In recent years, soft computing techniq

ues have fascinated

scientists and engineers all over the world because such te

chniques possess the

ability to learn and recall as similar to the main fun

ctions of the human brain.

They find better approaches to real world problems since

soft computing

incorporates human knowledge effectively. It deals with i

mprecision and

uncertainty and learn to adapt to unknown or changing

environment for better

performance. In neural network, adaptive resonance the

ory (ART1) gives good

results for binary MPIM CF problem. ART1 is not suitabl

e for non-binary input

pattern. Hence, in this work, suitable modification i

s included in the basic ART1

to incorporate the operational time of the parts, a r

atio level non-binary data. For

dealing with sequence of operations of the parts, an or

dinal 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 operati

onal time and operational

iii

sequence are combined and represented in a single matrix

. The modified ART1

used for solving CF problem with operational time is a

pplied to solve the problem

with combination of operational time and sequence. T

he CF problem without any

objective function is solved effectively by ART1 appro

ach.

For solving the CF problem with objective functions like

total cell load

variation (CLV) and exceptional elements, GA is propose

d 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 m

atrix. Both the objective

functions are combined to get a multi objective CF prob

lem and solved by using

GA. In the past, several performance measures like group

ing efficiency and

grouping efficacy have been proposed to find out the g

oodness of the output

clusters. But most of them are applicable only for binar

y data representation. In

this research work, suitable performance measures are propo

sed to measure the

goodness of the block diagonal structure of the output ma

trix 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 techniq

ues 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 fut

ure studies are also

addressed in this research.

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