JOURNAL OF INFORMATION, KNOWLEDGE AND RESEARCH IN
MECHANICAL ENGINEERING
CELL FORMATION APPROCHES/ TECHNIQUES – A
STUDY
H.D.VIRANI1, PROF. K.R.GAWANDE2 AND S.B.RAJAVIR3
1M.E.
(CAD/CAM) Student, Dept. of Mechanical Engineering/ C.U.Shah College of
Engineering and Technology/ Wadhwan City, Surendranagar, Gujarat, India.
2 A. Professor, Dept of Mechanical Engineering/ C.U.Shah College of Engineering and
Technology/ Wadhwan City, Surendranagar, Gujarat, India.
3 A. Professor, Dept of Mechanical Engineering/ Takshashila College of Engineering
and Technology/ Kalawad Road, Rajkot, Gujarat, India.
hiteshvirani@gmail.com
ABSTRACT : In the recent years, Cellular manufacturing has received considerable interest from practitioners
and academicians. Cellular Manufacturing System (CMS) has been emerged as a vital approach for batch and
job production environment. Cell Formation (CF), one major problem associated with CMS, involves the
process of Grouping the parts with design and manufacturing features into part families and corresponding
machines into Machine Cell. Group Technology has been an essential tool for developing a CMS. This paper
aims to discuss various approaches and techniques for CF and emphasized the significant research workdone in
past over the years and attempts to points out the gap in research of past studies.
Keywords : Cellular Manufacturing, Array Based Clustering, Fuzzy Clustering, Artificial Intelligence
1.
INTRODUCTION
Our manufacturing world continues to become more
complex brought principally global competition, cost
and profitability pressures, and rapidly advancing
technology.
Manufacturing industries are under great pressure
caused by the rising costs of energy, labor, capital,
and intensifying worldwide competition. While these
trends will remain for a long time, the problems
facing manufacturing today run much deeper. In
many cases they stem from the very nature of the
manufacturing process itself.
“Man is a user of tools. Those who recognize the
tools of tomorrow and learn how to use them today,
assure themselves of a place in tomorrow’s
prosperity.”
The concept of flexible automation evolved through
efforts to more efficiently and effectively utilize and
control assets, information, and resources in a
changing economic climate. Automated cells and
systems have grown from concept to reality in light
of installed systems currently in place throughout the
United States, Japan, and Europe. And they are
projected to grow dramatically over the next several
years as manufacturing strive to become more
comfortable with the increasing installed base of cells
and systems and the dynamic forces of
competitiveness, productivity and profitability.
Flexible manufacturing cells and systems represent
an avenue of change for manufacturers that helps to
bridge the gap between technology, competition and
profitability through a highly specialized and focused
approach to manufacturing effectiveness [1].
Cellular Manufacturing is an application of group
technology in which dissimilar machines or processes
have been aggregated into cells, each of which is
dedicated to the production of a part or product
family or a limited group of families. CM is one of
the major concepts used in the design of Flexible
manufacturing Systems. CM, also known as group
production or family programming, can be described
as a manufacturing technique that produces families
of parts within a single line or cell of machines.
Cellular Manufacturing (CM) has been proved as a
sound approach for improving operations in batch
and job shop environments. In cellular
manufacturing, Group Technology is used to form
part families based on similar processing
requirements. Parts and machines are then grouped
together based on sequential or simultaneous
techniques [2]. This approach results in cells where
machines are located in relative proximity based on
processing requirements rather than similar
functional
aspects.
Decision-making
and
accountability are more locally focused, often
resulting in quality and productivity improvements.
2.
APPROCHES
FOR
CELL
FORMATION
In general, Cell Formation Techniques can be
broadly classified.
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
Descriptive Procedures,

Cluster Analysis,

Graph Partitioning,

Artificial Intelligence,

Mathematical Programming

2.1 Descriptive Procedures
In general, descriptive procedures can be classified
into three major classes. The first class, which is
referred to as part families identification (PFI),
begins the cell formation process by identifying the
families of parts first and then allocates machines to
the families. The second class, which is referred to as
machine groups identification (MGI). The third class
of the descriptive procedures, which is referred to as
part families/machine grouping (PF/MG), identifies
the part families and machine groups simultaneously.
PFI methods can be sub classified as those based on
informal systems (e.g., rules of thumb, visual
examination or other criteria) and those based on
formal coding and classification systems. The role of
group technology (GT) codes in the context of
cellular manufacturing is primarily as an aid in
identifying the part families to which production cells
should be dedicated. Further analysis is required
before a family of parts to be manufactured in a cell,
and the machines, which will comprise that cell, can
be specified. MGI procedures consider the CF
problem as a two stage process where in the first
stage of their analysis, machines are grouped based
on information available in part routings and then in
the second stage, parts are allocated to machine
groups. When a CF approach attempts to group parts
into part families and machines into machine groups
simultaneously, then such an approach can be
classified as PF/MG. Burbidge [3] proposed one of
the earliest PF/MG descriptive approaches for the CF
problem which is referred to as Production Flow
Analysis (PFA). PFA is a technique, which analyses
the information given in route cards to form cells. A
manual method for CF called "Nuclear Synthesis" is
proposed where manufacturing cells are created
around "key machines". E1-Essawy [4] proposed a
method called Component Flow Analysis (CFA) at
about the same time. In some respects, the
methodology of CFA does differ from that of
Burbidge's PFA procedure since the latter first
partitions the problem, whereas the former does not.
2.2 Procedures Based on Cluster Analysis
Cluster analysis is composed of many diverse
techniques for recognizing structure in a complex
data set. The main objective of this statistical tool is
to group either objects or entities or their attributes
into clusters such that individual elements within a
cluster have a high degree of "natural association"
among themselves and that there is very little "natural
association" between clusters. Clustering procedures
can be classified as:
2.2.1 Array-based clustering techniques,
In array based clustering, the processing requirements
of components on machines can be represented by an
incidence matrix, as shown in Figure. 1. This is
referred to as the machine-component matrix
‘MPIM’. The machine-component matrix has zero
and one entries (aij). A "1" entry in row i and column
j (aij = 1) of the matrix indicates that component j has
an operation on machine i, whereas a "0" entry
indicates that it does not. The array based techniques
try to allocate machines to groups and components to
associated families by appropriately rearranging the
order of rows and columns to find a block diagonal
form of the aij = 1 entries in the machine component
matrix. The literature yields at least eight array-based
clustering algorithms, namely, Bond Energy Analysis
by McCormick et al. [5], Rank Order Clustering by
King [6], Modified Rank Order Clustering by
Chandrasekharan and Rajagopalan [7], Direct
Clustering Analysis by Chan and Milner [8], Cluster
Identification method by Kusiak and Chow [9].
2.2.2 Hierarchical clustering techniques
In hierarchical clustering, the data in the machinecomponent matrix are not partitioned into groups or
cells in one step. Rather they are first separated into a
few broad cells, each of which is further divided into
smaller groups, and each of these further partitioned,
and so on until terminal groups are generated which
cannot be subdivided. Essentially hierarchical
techniques may be subdivided into agglomerative
methods which proceed by a series of successive
fusions of the M machines or the P parts into groups,
and divisive methods which partition the set of M
machines (P parts) successively into finer groups. All
the agglomerative hierarchical techniques ultimately
reduce the data to a single cluster containing all the
machines (parts), and divisive techniques will finally
split the entire set of machines (parts) into M (P) cells
each containing a single machine (part). Hierarchical
classifications may be represented by inverted tree
structures or dendograms, which are two dimensional
diagrams illustrating the fusions or divisions, which
have been made at each successive stage of the
analysis. In the context of CF, only agglomerative
clustering techniques have been used. The most
widely used technique is single linkage. More
recently, the problem of "chaining" due to the use of
single linkage has been investigated and hence, the
average linkage algorithms have been recommended
for CF. A new hierarchical clustering algorithm for
CF referred to as the Set Merging algorithm has also
been proposed by Vakharia and Wemmerlov [10].
Fusions are based on similarities between machines
or parts.
2.2.3 Non-hierarchical clustering techniques.
Non-hierarchical clustering methods are iterative
methods and they begin with either an initial partition
of the data set or the choice of a few seed points. In
either case, one has to decide the number of clusters
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MECHANICAL ENGINEERING
in advance. Arbitrariness in the choice of seed points
(or initial partition of data) could lead to
unsatisfactory results. Non-hierarchical procedure has
been
developed
by
Chandrasekharan
and
Rajagopalan [7].
2.3 Graph Partitioning Approaches
Graph partitioning methods treat the machines and/or
parts as vertices and the processing of parts as arcs
connecting these nodes. These models aim at
obtaining disconnected sub graphs from a machinemachine or machine-part graph to identify
manufacturing cells. Rajagopalan and Batra [7]
suggest the use of Jaccard's similarity coefficients
and graph theory to form machine groups. Each
vertex in the graph represents a machine type and the
edge connecting vertices j and k is introduced in the
graph only if the "similarity" between the machine
types is greater than a pre specified threshold value.
After all allowable edges have been introduced,
cliques are formed. These cliques are then merged to
create cells so that intercellular moves are minimized.
An upper limit on cell size constrains the number of
machines in each partition. During the process high
and balanced machine utilization are strived for and
machine loads are used to determine the number of
machines of a given type needed for each cell.
Faber and Carter [11] developed a graph theoretic
algorithm for grouping machines and parts into
manufacturing cells by converting the machine
similarity matrix into a cluster network. The cluster
network is partitioned into cells by solving a
minimum cost flow problem. Kumar et al. [12]
developed a 0-1 quadratic programming with linear
constraints to solve the part grouping problem. The
quadratic model has been converted to two linear
problems and dealt with the k-decomposition
problem.
Askin and Chiu [13] proposed a cost-based
mathematical formulation and heuristic solution for
the CF problem. The Kernighan and Lin graph
partitioning method was adapted and applied in a two
phase partitioning algorithm. The first phase assigns
parts to specific machines. The second phase groups
machines into cells. Vohra et al. [14] proposed a
network-based algorithm to minimize the amount of
machining times performed outside the part primary
cells. Wu and Salvendy [15] developed a network
model to partition the machine-machine graph into
cells by considering operation sequences.
2.4 Artificial Intelligence Approaches
Elmaghraby and Gu [16] presented an approach for
using domain specific knowledge rules and a
prototype feature based modeling system to automate
the process of identifying parts attributes and
assigning the parts to the most appropriate
manufacturing cell. The expert assignment system is
based on the geometric features of the parts,
characteristics of formed manufacturing cells, parts
functional characteristics and attributes, as well as
domain specific manufacturing knowledge. Kusiak
[17] developed a pattern recognition based parts
grouping which is similar to the grouping in GT. The
basic difference between these two approaches is in
the degree of automation. Application of artificial
neural networks to CF problems has been proposed
by Karapathi and Suresh [18].
2.5 Mathematical Programming Approaches
Mathematical programming methods can be further
classified into three major groups based on the type
of formulation:
2.5.1 Linear programming (LP)
Linear programming is a concept of expressing the
interrelationship of activities of a system in terms of a
set of linear constraints in non-negative variables. A
program, that is, values of the variables is selected
which satisfies the constraints and premises a linear
objective function in these variables. LP based CF
methods have been proposed by Purcheck [19] and
Olivia-Lopez and Purcheck [20]. They essentially
apply the technique of combinatorial grouping and
LP to the CF problem. LQP models have been
proposed by Kusiak [17], Boctor [21] and many
others. GP models have been proposed by Shafer and
Rogers [22].
2.5.2 Goal programming (GP)
Goal programming is a model and associated
algorithm to minimize the absolute value of
deviations from a set of values called goals subjected
to technological constraints.
2.5.3 Dynamic programming (DP)
Dynamic programming determines the optimum
solution to “n” variable problems by decomposing it
into “n” stages with each stage constituting a single –
variable sub problem. The computational advantage
is that DP optimizes single-variable sub-problems.
However, because of the nature of the stage differs
depending on the optimization problem, DP does not
provide the computational details for optimizing each
stage.
3. SHORTCOMINGS OF PAST RESEARCH
There has been a lot research work done by various
researchers on cell formation techniques. Majority of
the published works on cellular manufacturing pay
very little attention towards production planning and
control activities of cellular manufacturing. Many
current cellular manufacturing applications are
running in a non optimal environment and their
performance could be improved by optimizing the
parameters. But from the available literature most of
the cell formation techniques/algorithm does not
discuss the optimal size of the cell and the optimal
number of cells, should be formed for a given
problem. The techniques discussed above also don’t
investigate the effect on different performance
measures if the number of cell / cell size/composition
of cells varied [23].
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4. CONCLUSION
Various techniques and methodology have been
briefly studied with their salient features. The study
brings the attention towards the need for designing
the cellular manufacturing system for optimal
performance as most of the past research work has
been concentrated to the clustering of the machine
and parts into cell and part families. So, acute need is
to develop the models to specify the optimal number
of groups and optimal production mix subject to
technological and logistical constraints for optimal
performance of cellular manufacturing system. There
is need to develop more efficient solution tools
enabling system designer to achieve good solution in
reasonable processing time.
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