Literature-survey

Develop cell layouts in the job-shop Literature Survey
Using Group Technology
Deepu Rajan 0004881061
Introduction
The rearrangement of existing departments in the job-shop without changing the
dedicated machines and its operation process is much difficult and complicated.
Group technology is a manufacturing terms which try to achieve some of the
operational advantages in a line layout by maintaining some of the strategic
advantages with in the job shop layout. One of the primary objectives for the
formation of cell layout in the job-shop is to reduce the material handling cost. Design
and development of cells in the job shop reduce the unnecessary movement of
machine parts and raw materials. This cell formation helps to re-design the job-shop
floor with machine sequence and repetitive lots. Cellular arrangement of machines
and its work flow analysis helps to eliminate the unwanted machines from the job
floor. Cellular Manufacturing enables the manufactures to reduce the engineering
cost, acceleration in product developments, simplified process planning, reduce the
usage of machines and tools.

Aim:
1. To find out the minimum capacity required for the existing machines in any
job shop to develop inter cellular layouts by using the Group Technology.
2. Improve the performance measures of machines in the job shop by
improving the machine compactness and bond efficiency.
3. Improve the efficiency of job shop by eliminating unwanted machines and
implementing Repetitive- lots.

Progress on the Research
The drawbacks from the previous research papers published on this selected research
topic have been identified. All the previous research on formation of cell layouts give
an exact idea how to make the cell arrangements using Group Technology in the job-
shop. It helps to understand how to develop cell layouts. The basic notations and
symbols, methods and algorithms developed and used for the formation of cells have
been learned. But none of the previous research on this topic has not mentioned what
is the minimum capacity required for a job-shop to form cell layouts. The previous
research papers have also failed to mention how to improve the performance measures
of machines used to form the cell layouts and efficiency of job-shops. Application of
Lean concept to the job-shops may help to improve better with ease in industrial
operational procedures.

Work to be done
 Select m number of machines and p number of parts to develop a machine part
matrix for the research purpose as an example case study.
 Develop a new algorithm using the basic notations, symbols and methods.
 Design the computational model of the new cell formed from the example case
study.

Summary
When completing each task of the research project its shows the scope of the selected
research topic. One of the toughest parts is to understand the notations, symbols,
formulas and methods used for the pervious research. While keep on analysing the
related topics through the journals, books and research papers will help to understand
the various applications of mechanical process related with the management concepts
and principles. From all this information’s and data’s collected help us to evolve and
develop new ideas, methods and techniques to improve better in each and every fields
of industrial applications. The introduction of lean concepts helps to remove the
unwanted process and make the process profitable.

Project scheduling: Notation, Classification Models and Methods.
For single item or small batch production project scheduling is concerned with its
dependent activities over time etc. in order to meet the lean management concepts the
capacities has been cut down by the companies by giving importance for make-to -
order project scheduling. The job-shop scheduling problem is considered as major
problem for the resource constrained project scheduling. Different variety of symbols
has been used by project scheduling researches in order to denote one and the similar
subject. A common notation and classification scheme has been used for both the
machine scheduling and project scheduling.

Some of the basic standard notations
Symbol Definitions
[Source: www.http://www.sciencedirect.com/science? _ob=ArticleURL&_udi=B6VCT-
3YSXJP1P&_user=6466718&_coverDate=01%2F01%2F1999&_alid=762402175&_rdoc=1&_fmt=
high&_orig=search&_cdi=5963&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000027518&_ve
rsion=1&_urlVersion=0&_userid=6466718&md5=4d185a68a04b6199f669dbcda085e2ce]

Machine scheduling with resource dependent processing times.
Alexander Gringoriew, Maxim Sviridenko, Mark Uetz (2007).
The objective of this method is to minimise the schedule make span by considering
different parallel machine scheduling settings. These settings are basically related to
parallel machine scheduling. The processing time of any job depends on the usage of
a scarce renewable resource. This model simplifies the traditional machine scheduling
problems by bringing a trade-off to the time resource. They used linear programming
rounding techniques to allocate resource to jobs and to reassign jobs to machines on
the basics of integer programming formulations. From these formulations it proved
the existence of constant factor approximation algorithm combined with Graham’s list
scheduling. These performance measures guaranteed to be (4+2√2) ≈ 6.83 for the
most case of unrelated parallel machine scheduling. This scheduling process
improved the bound for two special cases namely to (3+2√2) ≈ 5.83 whenever the
jobs are assigned to machines before hand and (5+ε) when the processing times do not
depend on the machine.
Applying lean principles for (HV/LV) high product variety and low volumes.
Jay Jina, Arindam K. Bhattacharya and Andrew. D. Walton (1997)
Lean manufacturing in the sense that the overall business process are organized or
rearranged to deliver products with greater quality and different variety using much
less resources and in shorter lead time that can be achieved by mass production
methods. But still the lean process has some limitations. The limitations include the
environmental and social conditions such as faster throughput time for in-put and out
put material flow. (WIP) work in process, small batch size production, more up time
with much less set-up and change over times, schedule stability is higher, and

rectification cost is less. Usually there are four types of factors which is should be
considered before applying lean principles to any manufacturing systems. Production
schedule will be changed as time gets closer to the delivery due dates. There should
be differences occurred between the product mix in case of multipoint, multimodal
facilities from one period to the next. Volume of the product may get changed
between the periods to aggregate the product mix. As per the customer lead time
expectations the degree and frequency of the product design will be changed. The
product variety can be maintained by using the common parts and modular designs,
which reduce the complexity among them. It is essential that the organizations must
develop the ability to re-invent their manufacturing systems. Multifunctional teams
can be used on directed activities effectively by exploiting the small size of HV/LV
organizations by the adoption of consistent performance measures. HV/LV has to
consider the initial measures such as batch sizes, space utilization, set-up times,
supplier delivery frequency, unplanned engineering changes, time, accuracy etc.
An analytical-interactive clustering algorithm for cell formation in cellular
manufacturing systems with ordinal level and ratio-level data.
Abraham.P.Geroge, Chandrasekaran, Rajendran, Soumyadip Ghosh (2003)
In this journal the problems of clustering machines into cells and components into
part families with the consideration of ratio-level and ordinal-level data. The ratio-
level data’s are characterized from the use of workload information’s obtained both
from per unit process times and production quantity of components and from machine
capacity. In case of ordinal-level data they considered the sequence of operations of
every component. These data sets are used in place of conventional binary data for
arranging clusters of cells and part families. A new approach to cell formation by
inspecting the machines and its part components, pointing the multi-dimensional
space with their co-ordinates defined by the corresponding elements in a machine-
component incidence matrix.

An effective P-median model considering production factors in machine cell/
part family formation.
Won, You Kyung, Currie, Kenneth .R (2006)
This journal is about the machine cell/ part family (MC/PF) formation in cellular
manufacturing. For most of the MC/PF cell formation, the mathematical programming
approach helps to find the optimum number of machine cells and their dependent part
families. The P-median model proved to be a powerful mathematical programming
model for solving the MC/PF problems. For grouping the machines and its parts a
new P-median formulation has been developed by considering the production factors
such as the operation sequence and production volume for parts. This P-median model
evolves a new similar coefficient based production factors that seriously affect the
MC/PF formation. While the existing P-median models depends on the traditional
binary part-machine incidence matrix. A production data based PMIM is used which
indicates the each non-binary entry to its actual intra-cell or inter-cell flows to or from
machine by parts.
Examples of Machine part matrix cell formation from the pervious research.
Part
Machine
Fig.1
Usually for cell formation a binary machine or part matrix of m*p dimension is taken
as a case. The p columns represent p parts and m rows represent m machines. The
m*p matrix indicates a relationship between parts and machines for each binary
elements. As the ‘1’ and ‘0’ indicates that the pth part should be worked on the mth
machine. Similar parts and machines are showed in the matrix. The main fact is to
group the machines and parts depend on their similarities. If we take the case of
machines or parts given in the above figure. The results are obtained by clustering of
method of cell formation.
1 0 0 1 0
0 1 1 0 1
1 0 0 1 0
0 1 1 0 1

Part
1 4 3 5 2
1
Machine 3
2
4
Fig.2
The results formed by clustering method shows that the machines 1 and 3 and the
parts 1 and 4 are in one cell while machine 2 and 4 and parts 3, 5 and 2 are in different
cells. In this case there are no ‘0’ inside the diagonal box and no ‘1’ outside the
diagonal box so it shows a perfect result.
Another example of machine part matrix but in this case the two cells are independent
so each family will be formed only within a group of machines.
Part
1 2 3 4 5
1
Machine
2
3
4
Fig.3
After clustering method of cell formation on the above part machine matrix. The
result obtained is
1 1 0 0 0
1 1 0 0 0
0 0 1 1 1
0 0 1 1 1
1 0 1 0 0
0 1 1 0 1
1 0 0 1 0
0 1 1 0 1

Part
1 4 3 5 2
1
Machine 3
2
4
Fig.4
[Sourse: http://www.sciencedirect.com/science/article/B6VCT-4NKXWJ9-
1/1/f8a17fb4955a1770a0d8c925ec2f5040? &zone=raall]
In this case ‘1’ is outside the diagonal block and part 3 is called an exceptional part as
it works on more than two machine groups. The machine 1 processes the two or more
part families it is called a bottleneck machine. The ‘0’ inside the diagonal block
indicates a void.
Cell formation clustering method is mostly used to obtain an optimal result or solution
under two conditions.
1) To minimise the voids i.e. is to reduce the number of 0’s inside the diagonal
blocks.
2) To minimize the exceptional elements i.e. to reduce the number of 1’s outside
the diagonal blocks.
Algorithms used for the cell formations
Kaparthi and C. Suresh (1992)
STEP 1. Give the vigilance parameter p and set the initial weights with
tij (0)=1 bij (0)= 1/ 1+n i= 1,……….. n, j = 1………c,
1 0 1 0 0
1 1 0 0 0
0 0 1 1 1
0 0 1 1 1

tij (t) is the top- down weight represents the centres and bij (t) is the bottom-up
connective weight between input nodes i and out put j at time t that is used to evaluate
matching scores.
STEP 2. Input a vector X
STEP 3. Use bottom-up weights to evaluate matching scores and determine the
winner according to the following j is the winner node when it satisfies node j = max
Where
STEP 4. Set and . Test if the measure
If the similarity measure is greater than ρ. Then go to step 6 or to step 5.
STEP 5. Disable the node j*
so that it will not become a candidate in the next iteration
and go to step 3. If there is no winner node then activate a new node and go to step 2.
STEP 6. Update the winner as follows
tij* ( t+1) = tij* (t) xi
bij (t+1) = tij* (t) xi / 0.5 + ∑ⁿ
i=1 tij* (t) xi
STEP 7. Go to step 2 until all the training data are inputted
The main drawbacks in this method are vigilance has greater stability and may result
in only one group. So a suitable vigilance parameter is important.
[Sourse:http://www.sciencedirect.com/science/article/B6VCT-4NKXWJ9-
1/1/f8a17fb4955a1770a0d8c925ec2f5040?&zone=raall]

Modified ARTI Algorithm
STEP 1. Determine the vigilance parameter ρ
ρ = ∑ⁿ
i=1 ∑ⁿ
j=i+1 │xi – xj │/ f (n) + ∑n-1
k=1 k
Take β = 0.5 and assign the first training vector to W1
STEP 2. Input the training vector X
STEP 3. Calculate the matching score to find the winner node j
*
by the following
equation node
TEST 4. Test the degree of Similarity
If ∑n
i=1 │Wij* - Xi │< ρ then go to step6 or to step 5
STEP 5. Active a new node and go to step 2
STEP 6. Update the winner as follows
Wij* ( t+1) + ( 1- β) x
STEP 7. Go to step 2 until all the training data as inputted.
[Sourse:http://www.sciencedirect.com/science/article/B6VCT-4NKXWJ9-
1/1/f8a17fb4955a1770a0d8c925ec2f5040?&zone=raall]

References
Asoo. J. Vakharia (2002) Method of cell formation in Group Technology (GT).
University of Aricona, Tuscon. U.S.A, 11 April 2002, ISSN: 0272-6963
John. B. Jensen, Manoj. K. Malhotra and Patrick. R. Philipoom (1999). Machine
dedication and process flexibility in Group Technology. School of Business,
University of Southern Maine, Portland, U.S.A. 23 February 1999. ISSN: 0272-6963.
John. L. Barbidge (2002).Production Flow Analysis for planning Group
Technology. Cranfield Institute of Technology, Cranfield, England. 17 April 2002.
ISSN: 0272-6963
John. Miltunburg and Wenjiang Zhang (2002). Algorithms for solving the cell
formation problem in Group Technology. 17 April 2002, ISSN: 0272-6963.
Urban. Wemmerlov (2002). Group Technology software’s. University of Wisconsin,
U.S.A, 12 April 2002 ISSN: 0272-6963. Journal of Operation Management, Volume
9, pages 500-525.
Mark Treleven and John. G. Wacker (2002). The Sources, Measurements and
Managerial Implications of process commonality. 17 April 2002. ISSN: 0272-
6963. Journal of Operation Management, Volume 7, pages 11-25.

Barbara. B. Flynn (2002). The use of sequence-dependent set-up time scheduling
procedure in Group Technology. 17 April 2002. ISSN: 0272-6963. Journal of
Operation Management, Volume 7, page 203-216
Nallan. C.and Jack. R. Meredith (2002). Achieving factory automation through
Group Technology principles. University of Cininnati, Ohio, U.S.A 12 April 2002,
ISSN: 0272-6963, Journal of Operation Management, Volume 5, pages 151-167.
D. J. Stockton, R .J. Lindley (1995). Implementing kanbans within high variety/
low volume (HV/LV) manufacturing environments. De Montfort University,
Leicester U.K Volume: 15, ISSN: 0144-3577.
Miin-Shen Yang, Jenn- Hwai Yang (2007),Dagli and Huggahalli Cell formation
using modified ART1 method. Chung Yuan Christian University Taiwan. 29 April
2007, ISSN: 0377-2217. Page 140-152, Volume-188
Janet Efstathiou, Peter Golby. Application of simple method of cell design for
product demand and operation sequence. Department of Engineering Science,
University of Oxford, U.K. ISSN: 0957-6061.
Jai kannan Janaki Jaganathan (2007). Solution to large facility layout problems
using Group Technology. Department of Industrial and Manufacturing Engineering,
Wichita state University, U.S.A May 2007.

V. Vitanov, B. Tjahjono and I. Marghalany (2007). A decision support tool to
facilitate the design of cellular manufacturing layouts. Department of
Manufacturing, School of applied Sciences, Cranfield University, U.K. Computers
and Industrial Engineering, Volume 52, pages 380-403 ISSN: 0360- 8352.
Hiroshiohta and Masaturu Nakamura (2002). Cell formation with reduction in set-
up times. Department of Industrial Engineering, College of Engineering, University
of Osaka Prefecture. 25 January 2002, ISSN: 0360-8352.
URL: www.Strategosinc.com/group-technology. Group Technology Simplifies
Lean Manufacturing.
Peter Brucker, Andrews Drexi Rolf Mohring, Klaus Neumann and Erwin Pesh (1999).
Project Scheduling : Notation, Classification models and methods. European
Journal of Operation Research , Volume 112, Issue 1, Pages 3-41, ISSN: 0377-2217.
Alexander Grigoriev, Maxim Sviridenko and Mark Uetz (2007).Machine Scheduling
with Resource Dependent Processing Times. Mathematical Programming. Volume
110, Pages 209-228. ISSN: 0302-9743.
Jay Jina, Arindam K Bhattacharya and Andrew D. Walton. (1997). Applying Lean
Principles to High Product Variety and Low Volumes. Logistics and Information
Management Volume: 10, ISSN: 0957-6053.
Abraham P George , Chandrasekharan Rajendran, Soumyadip Ghosh. (2003). An
Analytical- iterative Clustering Algorithm for Cell Formation in Cellular
Manufacturing Systems with Ordinal-level and ratio-level data. The International
Journal of Advanced Manufacturing Technology. Volume: 22, ISSN: 0268-3768.
Won, Youkyung, Currie, Kenneth R (2006). An effective P-median model
considering Production Factors in Machine Cell/ Part Family Formation. Journal
of Manufacturing Systems, Volume: 25, Pages 58-64, ISSN: 0278-6125.

URL:http://www.sciencedirect.com/science/article/B6VCT4NKXWJ91/1/f8a17fb4955a1770a0d8c925
ec2f5040?&zone=raall] for figures 1 -4.
URL:.http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VCT3YSXJP1P&_user=646
6718&_coverDate=01%2F01%2F1999&_alid=762402175&_rdoc=1&_fmt=high&_orig=search&_c
di=5963&_sort=d&_docanchor=&view=c&_ct=1&_acct=C000027518&_version=1&_urlVersion=0
&_userid=6466718&md5=4d185a68a04b6199f669dbcda085e2ce], for symbols and definitions
in page.7

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