850778

8/13/2019 850778

1/6

[ 377 ]

Integrated ManufacturingSystems9/ 6 [1998] 377382

MCB University Press[ISSN 0957-6061]

Redesigning jobshops to cellular manufacturingsystems

Godfrey C. OnwuboluNational University of Science and Technology, Bulawayo, Zimbabwe

Grouping parts into familieswhich can be produced by a

cluster of machine cells is thecornerstone of cellular manu-facturing, which in turn is the

building block for flexiblemanufacturing systems.

Cellular manufacturing is agroup technology (GT) con-cept that has recently

attracted the attention ofmanufacturing firms operat-ing in a jobshop environment

to consider redesigning theirmanufacturing systems so asto take advantage of

increased throughput, andreductions in work-in-

progress, set-up time, andlead times; leading to product

quality and customer satis-faction. Presents a similarityorder clustering technique forthe machine cell formation

problem. The procedure iscompared with many existingprocedures using eight well-

known problems from theliterature. The results show

that the proposed procedure,which is attractive due to itssimplicity, compares well with

existing procedures andshould be useful to practition-ers and researchers.

Introduction

Many manufacturing firms which hi therto

satisfied their customers while operating

jobshop production systems have recently

had to rethink because of the superi ori ty of

group technology (GT) philosophy of cellularmanufacturing. Cellular manufacturing

systems (CMS) house a large variety and

combinations of processes and shorten manu-

facturing lead times, reduce work-in-progress

(WIP), lower set-up times and operations

costs, improve quali ty, increase workers job

satisfaction and subsequent customer satis-

faction (Wemmerlov and Hyer, 1989). Manu-

facturing firms which embrace modern man-

ufacturing improvement philosophies such

as just-in-time (J IT), total quali ty manage-

ment (TQM), business process re-engineering

(BPR) and time-based competition (TBC) use

the principles of CMS i n their manufacturingsystem restructur ing efforts so as to meet

world-class manufacturing status

(Wemmerlov and J ohnson, 1997).

Solving flexible manufacturing systems

(FM S) optimal production planning and

scheduling problems through size reduction

require grouping of parts into part families at

the aggregate planning level (Bitran and Hax,

1981).

Planning and scheduli ng FMS at the disag-

gregate level involves machines, parts and

additional components such as tools, fixtures,

grippers and material handling components.The costs of these components are usually

high and they may be in l imited quantities.

Figure 1 shows the decomposition of FMS

planning situation at disaggregation level,

with all the parts to be manufactured and the

associated components being decomposed

into disjoint part and component famil ies.

This results in each of the individual part

and component famil ies being easier to man-

age. Individual part families are manufac-

tured by machine groups which utili se associ-

ated components such as the tools, fixtures,

grippers and material handling components

already mentioned.The grouping problem in GT involves diag-

onalisation of a given machine-part incidence

matrix into a cluster of machines which

produce a cluster of parts. In FMS, the

corresponding cluster of machines is referred

to as a flexible manufacturing cell (FMC),

while at the lower level i n the classical GT

physical cells, the cluster of machines is ordi-

nari ly referred to as a manufacturing cell .

Since a manufacturing cell is the building

block of FMC, the discussion in the remain-ing sections of this paper concentrate on

machine and part families grouping without

loss of generality.

Machine cell f ormation t echniques

Various approaches to cell formation have

since been reported and may fall under one of

the six major classifications: array-based

clustering, similarity coefficient, mathemati-

cal programming, graph and network, heuris-

tic and combinatorial optimisation.

There are two types of array-based cluster-ing techniques; rank order clusteri ng (ROC)

and bond energy analysis (BEA). In ROC, a

positional based value is assigned to each 1

in the machine-part incidence matrix and the

values of all the 1s in each row and each

column are summed, the rows and columns

being rearranged in decreasing order based

on the values from top to bottom, and from

left to right respectively (K ing, 1980). This

process is continued until an invariant

matrix is obtained resulting in a clusteri ng of

machines and parts along the diagonal.

Extensions to ROC to include cost data (Askinand Subramanian, 1987) and prevention of

positional based value getting extremely

large for large number of machines and parts

(King and Narkor nchai, 1982) have been

reported. Progressive slicing of identified

group after one iteration of row ranking and

one iteration of column ranking

(Chandrasekharan and Rajagopalan, 1986a)

have led to drastic improvement in ROC

results. The bond energy which is defined as

the product of the values of the adjoining row

and column elements in the machine-part

incidence matrix to determine the degree ofclustering has been successfully

implemented, with an optimal solution being

one which maximi ses the bond energy

(McCormick et a l., 1972).

8/13/2019 850778

2/6

[ 378 ]

Godfrey C. OnwuboluRedesigning jobshops tocellular manufacturing

systemsIntegrated ManufacturingSystems9/ 6 [1998] 377382

The first work to use similarity measures to

group machines and parts into cells

(McAuley, 1972) utili ses a similarity matrix

which contains all pairwi se similarity coeffi-

cients between each machine. The similarity

matrix obtained is then used by the single

linkage clusteri ng algori thm (SLCA) to formthe machine groups. These methods have

been adopted by other researchers (de Witte,

1980; Seifoddini and Wolfe, 1986; Tam, 1990;

Waghodekar and Sahu, 1984). The clustering

algori thms based on the similarity coefficient

fall into two classifications: hierarchical and

non-hierarchical. Both the single linkage

clustering algorithm and the average cluster-

ing algori thm are hierarchical clusteri ng

algori thms. The only known non-hierarchical

clustering reported so far include

MacQueens k-means method (MacQueen,

1967) which requires the number of clusters

to be specified in advance, the ideal-seed algo-

rithm (Chandrasekharan and Rajagopalan,

1986b) and its improved version ZODIAC

(Chandrasekharan and Rajagopalan, 1987).

Thep-median model based on an integer

programming formulation, which seeks to

maximise the sum of similari ty coefficients

for a fixed number of groups with the con-

straints that a part wi ll be assigned to not

more than one family, has been used to solve

the GT grouping problem (Kusiak, 1987).

Kusiaks similarity coefficients matrix has

been used as input to an assignment model

(Srinivasan et al ., 1990) to solve disjoint andnon-disjoint part famil ies problems. The part

grouping problem in GT has been modelled as

a linear transportation problem, solved in a

two-phase algori thm to approximate the

optimal solution (Kumar et a l., 1986). The p-

median formulation (Mulvey and Crowder,

1979) is an integer programming model wi th

an objective function which seeks to min-

imise the total sum of cluster-to-cluster

median distances.

Kumar et al ., (1986) formulated the GT

grouping problem as an optimal k-decomposi-

tion problem in graph theoretic terms in

which decomposition of networks are consid-

ered rather than block diagonalisation of

matri ces. Lenstra (1974) formulated the clus-

tering of a two-dimensional data array as

equivalent to solving two travelling salesman

problem. This problem has been mapped as a

k-decomposition of weighted networks which

is NP-complete (Ravikumar et a l., 1986).Several heuri stics which give polynomial

bounds on the required computation time,

but which do not guarantee optimal solution

include Ballakur and Steudel (1987); Kumar

and Vanell i (1987); Tabucannon and Ojha

(1987), to mention a few.

Other approaches that have been suggested

by researchers include the use of neural net-

works (Suresh and Kaparthi , 1994; Venugopal

and Narendran, 1994), simulated annealing

(Hindi and Haman, 1994; Venugopal and

Narendran, 1992). For more on these see

Singh and Rajamani (1996).The present study solves medium to large

binary machine-part incidence matrix prob-

lems and also another class of problems

where machine uti li sations are considered,

based on the simi larity order clustering

(SOC) algorithm.

The SOC procedure

Determining similaritySimi larity coefficients (SCs) define relation-

ships between pair s of machines or parts. The

closer the relationship is, the higher the SC.

The SCs are determined for both machines

and parts from the machine-part incidence

matrix. The SC used in the work reported in

this paper is the one by Kusiak (1987).

Kusiaks SC for machines is as follows:

where si j

is the similari ty coefficient between

machines i andj, ai k

is the machine-part inci-

dence matrix value which is non-zero i f kthpart visits ith machine and is zero if other-

wise. The diagonal values of the square simi-

lari ty coefficient matrix are all zero. A simi-

lar matrix is generated for parts.

s d V d a a

s V

i j k i j

k

m

ki k i j

i j j

= = =

=

=

; ,1

1

0

if

0 if otherwise

Component family #1

FMS

Part family #1

Component family #2

Part family #2

Component family #n

Part family #n

Figure 1

Decomposition of FMS planning at

disaggregation level

8/13/2019 850778

3/6

[ 379 ]



Based on Kusiaks coefficients, the SC

between machines 3 and 4, s34 in Figure 2, can

be calculated as follows: machines 3 and 4 do

not process parts 2, 3, 5, 7, 8, 10, 11, 12, 13, 15and do process parts 1, 9, 14.S34 is therefore 10

+ 3 or 13. An si j

is determined for all possible

pairs of machines. The same procedure is

followed for parts. The similari ty coefficients

for machines in Table I are given in Table II .

Using the SOC to obt ain machine and partsequencesSOC uses the order or rank of the simi larity

coeffi cient value (SCV) to determine the best

sequence for a given problem. Three steps are

involved:

1 Order the SCVs in a decreasing manner.2 Obtain the topology matrix.

3 Generate the sequence from the topology

matrix.

Step 1 involves scanning the similarity coeffi -

cient matrix given in Table II . It is useful to

represent the simi larity coefficient matrix as

a half-triangle, having values contained in

the upper part of the diagonal and the lower

part of the diagonal being zero. The SCVs are

15, 14, 13, 7, 6, 5.

Step 2 involves reading the row and column

numbers of all ordered non-increasing SCVs

into the topology matri x. For example, SCVs

of 15 correspond to pairwise labels of (2, 5),(2, 8), (5, 8) and (7, 10). The first value corre-

sponds to the row number whi le the second

value of the pair cor responds to the column

number in the similarity coefficient matrix.

The topology matrix is given in Table III .

In step 3, the first pair in the topology

matrix are taken into the machine sequence.

Hence in the first i teration in this step, the

partial sequence array Q holds two machine

labels: Q = {2, 5}. A closed loop is obtained by

searching through the topology matrix, so

that Q = {2, 5, 8}. This corresponds to

machines in one cell C1= {2, 5, 8}. In the sec-ond iteration, the labels 2, 5, 8 are avoided

during the search but a new pair in the topol-

ogy matrix are chosen so that the partial

sequence array Q holds two machine labels: Q

= {7, 10}. A closed loop is obtained by search-

ing through the topology matrix, so that Q =

{7, 10, 1}. This corresponds to machines in

another cell C2= {7, 10, 1}. In the thi rd itera-

tion, the labels 2, 5, 8, 7, 10, 1 are avoided dur-

ing the search but a new pair in the topology

matrix are chosen so that the parti al

sequence array Q holds two machine labels: Q

= {3, 6}. A closed loop is obtained by search-

ing through the topology matrix so that Q =

{3, 6, 4, 9}. This corresponds to machines in a

thi rd cell C3 = {3, 6, 4, 9}. Since the total num-

ber of machines in all the cells are equal to

the given number of machines, the algorithm

terminates generating sequences for the

machines. The same steps are followed for

determining parts sequence. We note that the

SOC determines the best sequence for

machines within the SCV range of 15 and 14.

The next lower SCV is 7 followed by the least

value of 6. SOC ignores these because they are

too low to give good machine clustering. Thi s

results in the final machine-part matrix inTable IV. Machines {2, 5 and 8} form the first

cell, machines {7, 10, 1} form the second cell

and machines {3, 6, 4, 9} form the thi rd cell.

The corresponding parts that form the three

part-fami lies are {5, 3, 8, 13, 15}, {10, 2, 11, 12,

7}and {14, 9, 1, 4, 6} respectively.

Comparison with other algorithms

Problem setIn order to test the effectiveness of the SOC

procedure, eight problems from the li terature

were used as a problem set. The differentproblems and their characteristics are shown

in Table V. Problems 1, 3, 4, 5 and 6 were

solved by Miltenburg and Zhang (1991) using

nine different algorithms. The SOC

Table II

Similarity coefficients for the SOC example(aij=aji)

M achine M achine

1 2 3 4 5 6 7 8 9 10

1 0 6 7 7 6 6 14 6 7 142 0 6 6 15 5 5 15 6 53 0 13 6 14 6 6 13 64 0 6 14 6 6 13 65 0 5 5 15 6 5

6 0 5 5 14 57 0 5 6 158 0 6 59 0 6

10 0

Table IA 10 15 machine-part incidence matrix

M achine Par t

1 2 3 4 5 6 7 8 9 1 0 1 1 12 1 3 14 15

1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 02 0 0 1 0 1 0 0 1 0 0 0 0 1 0 13 1 0 0 0 0 1 0 0 1 0 0 0 0 1 04 1 0 0 1 0 0 0 0 1 0 0 0 0 1 05 0 0 1 0 1 0 0 1 0 0 0 0 1 0 16 1 0 0 1 0 1 0 0 1 0 0 0 0 1 07 0 1 0 0 0 0 1 0 0 1 1 1 0 0 08 0 0 1 0 1 0 0 1 0 0 0 0 1 0 19 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0

10 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0

8/13/2019 850778

4/6

[ 380 ]



procedure results for these problems are

compared to the results in that paper. In addi-

tion, problems 2, 3, and 4 were solved by

Askin et al . (1991) using the HPH method.

Since they reported results better than that of

the ROC2 algori thm, the SOC results from

these problems are also compared to the HPH

method results. Finally, problems 7 and 8

were solved optimally by Wei and Gaither

(1990). The SOC performances for these two

problems are compared to the optimal solu-tions.

Performance measures for cell groupingsolutionsThe quali ty of the solution presented in the

form of machine-part incidence matrices is

not immediately obvious. Therefore, a

method is needed to compare these matrices

using evaluation measures. Two measures

used by Miltenburg and Zhang (1991) are used

here. They are the grouping efficiency mea-

sure the bond energy measure. These

measures are chosen because together they

measure the within-cell uti li sation, inter-cell

movement, and the abili ty to cluster the non-

zero entries of the machine-part incidence

matri x together. Thus, they are comprehen-sive in their evaluation of cell grouping. The

first measure is considered to be the primary

measure, whi le the second measure is consid-

ered to be a secondary measure.

The grouping efficiency measure (e)Denoting the number of non-zero entr ies in

the diagonal block (which form the cells) in

the machine-part incidence matrix as n1,Miand P

ias the number of machines and parts

in each cell i, respectively, then

whereCis the number of cells in the matrix

and e1 is an indicator of the within-cell den-

sity of a cell. Higher values ofe1show greater

simi larity between the machines or parts in

this cell , and ideally, it is expected that the

whole cell be filled with non-zero entries. An

inter-cell transfer indicator is defined as

where n0 is the number of non-zero entri es in

the machine-part incidence matrix outside

the cells or inter-cell transfers. Lower values

of n0and consequently values of e2 indicate

fewer i nter-cell transfers and better solutions.

The grouping efficiency is given as the differ-

ence between e1and e2:

In Table IV, there are three manufacturing

cells with 15, 14 and 17 non-zero entries in

each cell, respectively. The sum of these is 46

(n1). The first two cells have three machines

and five parts each, and the last cell has four

machines and five parts. Summing the prod-ucts of the machines and parts for the three

cells is 3 5 + 3 5 + 4 5 = 50. Thus, e1 is 46/ 50

or 0.92. The number of inter-cell transfers, n0= 0, hencee2= 0. Thus, e= 0.92 0.0 = 0.92

e e e e = 1 2 1 1 ,

en

n n2

1

1 0

1=+

,

en

M Pi ii

c11

1

=

=

,

Table III

Topology matrix T (i,j) the for SOC example

T ( i, j) 2,5 2,8 5,8 7,10 1,7 1,10 3,6 4,6 6,9 1,3 9,10 8,10SCV 15 15 15 15 14 14 14 14 14 7 6 5

Table I V

Cell grouping for 10 15 machine-incidence matrix of Figure 1

M achine Par t5 3 8 13 15 10 2 11 12 7 14 9 1 4 6

2 1 1 1 1 15 1 1 1 1 18 1 1 1 1 17 1 1 1 1 110 1 1 1 1 11 1 1 1 1 03 1 1 1 0 16 1 1 1 1 14 1 1 1 1 09 1 1 0 1 1

Table VTest problem set

M achine Par t s M at rix

Number Problem (m) (n) densit ya

1 Chan & Milner (1982) 15 10 0.312 De Witte (1980) 12 19 0.333 Chandrasekharan &

Rajagopalan (1986b) 8 20 0.384 Burbridge (1975) 16 43 0.185 Carrie (1973) 20 35 0.196 Chandrasekharan &

Rajagopalan (1986a) 8 20 0.577 Kumar and Vanelli (1987) 30 41 0.1058 King (1980) 14 24 0.175

Note:aThe matrix density is given by [mn nj aij] / (mn) where aij0 in the

machine-part incidence matrix, m is number of machines andn is number of parts.

8/13/2019 850778

5/6

[ 381 ]



The bond energy measureClustering algorithms bri ng the non-zero

entries of the machine-part i ncidence matrix

close to each other. Thi s is essential ly theobjective of the bond energy algorithm (BEA).

The strength of clustering can be computed

byb, the normalised bond energy measure of

a matrix given by

Higher value bshows that the non-zero enter-

ies of the machine-part incidence matrix are

closely li nked. In the example in F igure 3, bis

1.41.

Results

The results presented here for the SOC proce-

dure are those obtained by visual analysis of

the soluti ons provided. The results shown inTable VI compare the SOC results with the

SC-seed of Miltenburg and Zhang (1991), the

HPH method (Askin et al ., 1991) and 0-1 pro-gramming (Wei and Gaither, 1990). In Mil-

tenburg and Zhang (1991), wi th respect to the

five problems presented in the work reported

here, the SC-seed performed best with respectto the grouping effi ciency measure. In prob-

lems 1, 3, 5 and 6, the SOC grouping efficiencymeasure is vir tually identical to that of theSC-seed algori thm. Apart from problem 5, the

SOC bond energy i s better than the SC-seed

algorithm.In problem 3, the SOC grouping efficiency

measure is the same HPH method but inferior

for problem 4. In problems 7 and 8, the group-

ing efficiency measure is better that the opti-mal solutions of the 0-1 programming algo-

rithm in Wei and Gai ther (1990) where con-

straints existed on the number of machines ina cell . In problem 7, where the constraint on

the number of machines in a cell is 20 or less

in Wei and Gaither (1990), the SOC procedure

remains a four-cell solution, whi le the 0-1

programming procedure identifies a two-cell

solution.

Conclusion

The results of the experiments carri ed out

show that the SOC procedure is effective for

problems with different matrix densiti es. In

all but one test problems established in the

li terature, it performed well on the grouping

measure which i s the primary measure of the

quality of the solution. Thi s measure is an

indicator of the abili ty of an algorithm to

cluster random machine-part incidence

matrices such that identification of cells

becomes obvious. Since Wemmerlov and Hyer(1989) reported that may companies

performed manual analysis of the machine

cell grouping problem, the abili ty of an algo-

ri thm to form clusters wil l ensure that good

solutions to perform the manual analysis of

cell formation that meet the requirements of

the parti cular environment are available to

the decision maker. The SOC procedure also

informs the decision maker on the machine

utili sation so that the number of machines

required to meet the demand can be known

before the actual implementation of the man-

ufacturing system reconstruction from job-

shop to manufacturing cells. The data

required for determining the machine util isa-

tion include the machine processing time for

parts, setup time per batch, part batch sizes,

period demand for parts and time available

per machine per period.

The SOC procedure is very attractive

because of i ts simplicity and yet it i s quite

effective. All that is required of the user is to

create a data file containing the number of

machines, number of parts and the machine-

part incidence matrix. The SOC procedure

dumps the solution in an output file which

will then be accessed by the user. This is veryuseful, given that companies prefer simple

but effective cell grouping procedures.

ReferencesAskin, R.G. and Subramanian, S.P. (1987), A cost

based heuristic for group technology configu-

ration, In ter nat ional Jour nal of Product ion

Resear ch, Vol. 25 No. 1, pp. 101-13.

Askin, R.G., Cresswell, J .H., Goldberg, J .B. and

Vakharia, A.J . (1991), A Hamil tonian path

approach to reordering the part-machine

matrix for cellular manufacturing,

In ter nat ional Jour nal of Product ion Research,

Vol. 29 No. 6, pp. 1081-100.

Ballakur, A. and Steudel, H.J . (1987), A within-cell

utili sation based heuristic for designing cellu-

lar manufacturi ng systems, In ternat ional

Jour nal of Produ ction R esearch, Vol. 25 No. 5,

pp. 639-65.

b a a a a a ij i j ij i i j j

n

i

m

j

n

i

m

ij

j

n

i

m

= +

+ +==

=

= == ( ) ( ) /1 1

11

1

1

1

1 11

Table VI

Results

SOC SC-see d HPH meth od 0 -1 p ro grammin g

No. of

Problem cells e1 e2 e b e b e b e b

1 3 0.92 0.0 0.92 1.41 0.93 1.37 2 2 0.57 0.28 0.29 1.0 3 3 1.0 0.15 0.85 1.34 0.85 1.31 0.85 1.38 4 2 0.3 0.2 0.14 0.63 0.45 1.02 0.42 1.11 5 4 0.87 0.21 0.66 1.18 0.76 1.40

6 1 0.57 0.0 0.57 1.10 0.57 1.21 2 0.77 0.37 0.40 0.83 7 4 0.39 0.13 0.26 0.60 0.16a 0.918 2 0.62 0.29 0.33 0.67 0.33 1.08Note: aTwo cell solution

8/13/2019 850778

6/6

[ 382 ]



Bi tran, G.R. and Hax, A.C. (1981), On the design of

hierarchical production planning systems,

Decision Sciences,Vol. 8, pp. 717-43.

Burbridge, J .L . (1975),Th e In t roduct ion of GroupTechnology,J ohn Wiley, New York, NY.

Carrie, A. (1973), Numerical taxonomy appli ed to

group technology and plant layout, I n te rna-

t ional Jour nal of Production Resear ch, Vol. 11

No. 4, pp. 399-416.

Chan, H.M . and Milner (1982), Direct clustering

algori thm for group formation in cellular

manufacturing, Journa l o f Manufactu r i ng

Systems, Vol. 1 No. 1, pp. 65-74.

Chandrasekharan, M.P. and Rajagopalan, R.

(1986a), MODROC: an extension of rank order

clusteri ng for group technology, I n te rna-


No. 5, pp. 1221-33.

Chandrasekharan, M.P. and Rajagopalan, R.(1986b), An i deal seed non-hierarchical clus-

tering algorithm for cellular manufacturing,

In ter nat ional J our nal of Production Research,

Vol . 24 No. 2, pp. 451-64.

Chandrasekharan, M.P. and Rajagopalan, R. (1987),

ZODIAC an algori thm for concurrent formu-

lation of part families and machine cells,

In ter nat ional Jour nal of Product ion Research,

Vol . 26 No.6, pp. 835-50.

De Witte, J . (1980), The use of simi lar ity coeffi -

cients in production flow analysis,I n ter na-


No. 4, pp. 503-14.

Hi ndi, K.S. and Hamam, Y.M. (1994), Solving the

part famil ies problem in discrete parts manu-facture by simulated annealing,Production

Plann ing and Contr o l, Vol. 5. pp. 160-4.

King, J .R. (1980), Machine component grouping in

production flow analysis: an approach using

rank order clustering, I n ter na t iona l Journa l

of Pr oduction Resear ch, Vol. 18, pp. 213-32.

King, J .R. and Nakornchai, V. (1982), Machine

component group formation in group technol-

ogy review and extension, In ternat ional

Jour nal of Production Resear ch, Vol. 20,

pp. 117-33.

Kumar, K.R. and Vannelli , A. (1987), Strategic

subcontracting for efficient disaggregated

manufacturing, I n ter na t iona l Journa l o f

Pr oduction Resear ch, Vol. 25 No. 12, pp. 1715-28.

Kumar, K.R., Kusiak, A . and Vannell i, A. (1986),

Grouping of parts and components in flexible

manufacturing systems,Eur opean Jour nal of

Operati onal R esear ch, Vol. 24 No. 2, pp. 387-97.

Kusiak, A . (1987), A generalised group technology

concept, In ter nat ional Jour nal of Product ion

Resear ch, Vol. 25, pp. 561-9.

Lenstra, (1974), Clustering a data array and the

travelling salesman problem,Operations

Resear ch, Vol. 22, pp. 413-14.

McAuley, J . (1972), Machine grouping for effi cient

production,Pr oduction Engi neer, Vol. 51, p. 53.

McCormick, W.T., Schweitzer, P.J . and White, T.W.

(1972), Problem decomposition and data reor-

ganisation by a clustering technique,Opera-

ti on R esear ch,Vol. 20, pp. 993-1009

MacQueen, J .B. (1967), Some methods for

classification and analysis of multivariate

observations, Pr oceedi ngs of th e Fi fth Sym po-

sium on M athemat i ca l Stat is t ics and Probabi l -

ity, University of Cali fornia, Berkeley, Vol. 1, p.

281.Miltenburg, J . and Zhang, W. (1991), A comparative

evaluation of nine well-known algori thms for

solving the cell formation problem in group

technology, Jour nal of Operat i ons M anage-

ment, Vol. 10 No. 1, pp. 44-69,

Mulvey, J .M. and Crowder, H.P. (1979), Clustering

analysis: an application of Lagrangian relax-

ation,M an agement Science, Vol. 25 No. 4,

pp. 329-40.

Ravikumar, K ., Kusiak, A . and Vannell i, A. (1986),

Grouping of parts and components in flexible

manufacturing systems,Eur opean Jour nal of

Operat ion al Resear ch, Vol. 24, pp. 387-97.

Seifoddini, H. and Wolfe, P.M. (1986), Application

of the similarity coefficient method in group

technology, I I E Tr ansact ions,Vol. 18 No. 3,

pp. 271-7.

Singh, N. and Rajamani, D. (1996),Cel lu la r M anu-

factur in g Systems Design, Plann in g and Con-

t ro l , Chapman & Hall, London.

Srinivasan, G., Narendran, T.T. and Mahadevan, B.

(1990), An assignment model for the part-

families problem in group technology,In ter -

nati onal Jour nal of Produ ction Resear ch,

Vol. 28 No. 1, pp. 145-52.

Suresh, N.C. and Kaparthi , S. (1994), Performance

of fuzzy ART neural network for group technol-

ogy cell formation, I n tern a t iona l Journa l o f

Pr oduction R esear ch,Vol. 32, pp. 1693-713.Tabucanon, M.T. and Ojha, R. (1987), ICRM A a

heuristic approach for i ntercell flow reduction

in cellular manufacturi ng systems,M ater ia l

Flow, Vol. 4, pp. 189-97.

Tam, K.Y. (1990), An operation sequence based

similarity coefficient for part famili es forma-

tions, Journ al of M anufactur i ng Systems,

Vol. 9 No. 1, pp. 55-68.

Venugopal, V. and Narendran, T.T. (1992), Cell

formation in manufacturing systems through

simulated annealing: an experimental evalua-

tion, Eur opean J ourna l of Operat i onal

Research,Vol. 63, pp. 409-22.

Venugopal, V. and Narendran, T.T. (1994),

Machine-cell formation through neural net-work models, I n tern a t iona l Journa l o f Pro-

du ction R esear ch,Vol. 32, pp. 2105-16.

Waghodekar, P.H. and Sahu, S. (1984), Machine-

component cell formation in group technology:

MACE, In ternat i onal Journ al of Product ion

Research,Vol . 22 No. 6, pp. 937-48.

Wei, J .C. and Gaither, N. (1990), An optimal model

for cell formation decisions,Decision Sciences,

Vol. 21 No. 2, pp. 416-33.

Wemmerlov, U. and Hyer, N.L., (1989), Cellular

manufacturi ng in the US industry: a survey of

user, In ternat i onal Journ al of Product ion

Research,Vol. 27, pp. 1511-30.

Wemmerlov, U. and J ohnson, D.J . (1997), Cellular

manufactur ing at 46 user plants: implementa-

tion experiences and performance improve-

ments, In ter nat ional Jour nal of Product ion

Research , Vol . 35 No. 1, pp. 29-49.

850778

Documents

Transcript of 850778