Teerawut Tunnukij
Christian Hicks
1
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Road Map
• Components of GAs
• Problems of the classical
GAs for solving the cell
formation problem
Clustering
methods
GGAs
GAs
Benefits of GT/CM
to facilities layout
design
Facilities
layout design
GT/CM
Comparisons &
performance
Developed
GGA
General structure &
components of the
developed GGA
Goal
Performance &
benefits of the
proposed GGA
• General problems of
clustering methods
• Suitable methods for
the solutions
Start
2
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The facilities layout design
Job Assignment
Select machines for each operation
and specify operation sequences
Cell Formation
Group machines into cells
Layout Design
Assign cells within plants and
machines within cells
Transportation
System Design
Design aisle structure and select
material handling equipment
3
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Group Technology & Cellular Manufacturing
Clustering
Methods
have been used
for identifying
Manufacturing
cells
Based upon
Group
Technology
A philosophy that aims to exploit
similarities and achieve efficiencies
by grouping.
GT has been applied to manufacturing systems
known as Cellular Manufacturing (CM).
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Manufacturing Layout
Process (Functional) Layout
Group (Cellular) Layout
A cluster
or cell
T
T
M
M
T
T
M
M
T
T
D
D
CG
SG
CG
T
T
T
SG
M
M
T
D
D
M
D
SG
CG
CG
D
D
D
Like resources placed together
M
D
D
SG
Resources to produce like
products placed together
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The benefits of CM
Main benefits
• Reduced throughput time
• Reduced work in progress
• Improved material flows
Cellular
Manufacturing
Others
• Reduced inventory
• Improved use of space
• Improved team work
• Reduced waste
• Increased flexibility
Reduced
Manufacturing
Costs
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Clustering Methods
A large number of
clustering methods
have been developed
Can be
classified
into
Part family
grouping
Form part families and
then group machines
into cells.
Machine
grouping
Form machine cells
based upon similarities
in part routing and then
allocate parts to cells.
Machine-part
grouping
Form part families and
machine cells
simultaneously.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Clustering Methods
Part family grouping
Classification
& Coding
Meta-heuristic
Methods
Machine grouping
Similarity coefficientbased Methods
Heuristic
Methods
Graph
theoretic
Mathematical
Programmingbased Methods
Machine-Part incidence
matrix-based Methods
Machine-part grouping
• Most of these methods have exploited the machine-part matrix as
the initial information to identify potential manufacturing cells.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
A machine-part incidence matrix
Exceptional
elements
1
2
3
4
1
1
1
1
2
1
1
3
1
1
4
1
1
1
Parts
5
1
1
(a) the original matrix
6
1
1
Machines
Machines
Parts
2
1
4
3
1
1
1
1
3
1
1
6
1
1
2
4
1
5
1
1
1
1
1
1
(b) a rearranged matrix
into block-diagonal forms
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
General problems of clustering methods
Conventional methods do not
always produce a desirable
solution.
There are many ‘exceptional
elements’ (machines & parts that
cannot be assigned to cells).
The cell formation problem has
been shown to be a nondeterministic polynomial (NP)
complete problem.
Meta-heuristic
methods
• Good methods
for the solution
• SA, TS, GAs
11
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Genetic Algorithms (GAs)
• GAs are one of the meta-heuristic algorithms. They are
stochastic search techniques for approximating optimal
solutions within complex search spaces.
• The technique is based upon the mechanics of natural
genetics and selection.
• The basic idea derived from an analogy with biological
evolution, in which the fitness of individual determines
its ability to survive and reproduce, known as ‘the
survival of the fittest’.
12
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
GAs: The main components
1. Genetic representation
5. Genetic operators
2. Method for generating
the initial population
6. Mechanism for creating
successive generations
GAs
3. Evaluation function
7. Stopping Criteria
4. Reproduction selection
scheme
8. GA parameter settings
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
GAs: The cell formation problem
• Venugopal and Narendran (1992) were the first researchers to apply
GAs to the cell formation problem.
The general chromosome representation
6 parts (or machines)
1
2
3
4
5
6
1
1
2
3
2
1
Cell number
A potential solution
Chromosome:
Cell 1: 1,2,6
Cell 2: 3,5
Cell 3: 4
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
GAs: The problem of the classical GAs
• The standard gene encoding scheme includes significant
redundancy when representing a grouping problem (Falkenauer 1998)
A
B
A
C
C
A
C
B
1
2
1
3
3
1
3
2
All chromosomes
represent the
same solution
This repetition problem
• increases the size of the
search space;
• reduces the effectiveness
of the GAs.
15
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Grouping Genetic Algorithms (GGAs)
• The GGA, introduced by Falkenauer (1998), is a specialised
GA tool that has been adapted to suit and handle the
structure of grouping problems.
• The GGA differs from the classical GAs in two important
aspects:
1. The special gene encoding scheme;
2. The special genetic operators.
• De Lit et al. (2000) first applied the GGA to solve the cell
formation problem with the fixed maximum cell size.
16
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The developed GGA: The general structure
1
Start
Encode
Genes
2
Generate
Population
Integer representing Randomly combine
a cell number
genes with a repair
process
Population
3
Chromosome
Random
selection
7
Yes
Create population
for the next
No generation
Terminate?
Genetic Operation
Crossover operation
Parent 1
Offspring 1
Parent 2
Offspring 2
Chromosome
Stop
Number of
generation
4
Mutation operation
Parent 1
Offspring 1
Chromosome
4.1
6
Chromosome
selection
Roulette
Wheel
5
Evaluate Fitness
Repair Process
Check & remove
empty cells
Check no. of cells
2≤C≤min(M-1,P-1)
Check & replace
duplicate cell no.
Check & relocate
unassigned parts
& machines
Grouping efficacy
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The analysis of performance
Best Fitness
1
2
3
4
5
1
1
2
3
1
1
1
1
1
1
4
1
5
6
1
1
1
1
1
1
7
1
8
Fitness
A simple CFP
1
1
1
1
Avg. Fitness
0
-0.5
-1
-1.5
-2
-2.5
-3
-3.5
-4
-4.5
-5
the performance of
the GGA proposed by
Yasuda, et al. (2005)
1
3
5
7
9
11
13
15
17
19
Generation
(a) The 5x8 original matrix
Best Fitness
Avg. Fitness
1.0
4
1
1
1
7
1
1
1
2
3
5
6
8
0.8
Fitness
1
4
5
2
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
(b) The 5x8 matrix after clustered
0.6
the performance of
the developed GGA
0.4
0.2
0.0
1
3
5
7
9
11
13
15
17
19
Generation
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The analysis of performance
Comparisons of five clustering algorithms
Data set
Size
ZODIAC
GRAFICS
Cheng &
others’ GA
CF-GGA
Yasuda and Developed
others’ GGA
GGA
CR1
24×40
100.00
100.00
100.00
100.00
100.00
100.00
CR2
24×40
85.11
85.11
85.11
85.11
85.11
85.11
CR3
24×40
73.03
NA
73.03
NA
73.03
73.51
CR4
24×40
73.51
73.51
NA
73.29
73.51
73.51
CR5
24×40
20.42
43.27
49.37
48.98
48.98
53.21
CR6
24×40
18.23
44.51
44.67
46.81
45.00
46.04
CR7
24×40
17.61
41.61
42.50
44.14
41.90
43.66
KN1
16×43
53.76
54.39
53.89
53.70
55.43
56.88
• CR1-CR7 obtained from Chandrasekharan and Rajagopalan (1989)
• KN1 obtained from King and Nakornchai (1982)
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
The analysis of performance
100.00
90.00
Grouping efficacy
80.00
70.00
ZODIAC
60.00
GRAFICS
Cheng & others’ GA
50.00
CF-GGA
40.00
Yasuda and others’ GGA
Developed GGA
30.00
20.00
10.00
0.00
CR1
CR2
CR3
CR4
CR5
CR6
CR7
KN1
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Conclusions
• The developed GGA including a repair process was developed for
solving the CFP without the predetermination of the No. of
manufacturing cells and the No. of machines within the cell.
• The developed GGA was applied to well-known data sets from the
literature and was compared to other methods. The results show the
developed GGA is effective, performs very well, and outperforms
other selected methods in most cases.
• The designed parameter experiment suggests that the large no. of
population size have more chance to obtain the better solution, and
using the range 0.6-0.7 for probability of crossover and the range
0.2-0.3 for probability of mutation tends to produce the better
solution.
27
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
Further Work
• Develop the proposed GGA to be able to consider important
parameters such as operation sequences and others.
• Apply the developed GGA to a data set obtained from a
collaborating company.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
29
Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
References
Aytug, H., Khouja, M. and Vergara, F. E., 2003, Use of genetic algorithms to solve production
and operations management problems: A review, International Journal of Production
Research, 41(17), 3955-4009.
Brown, E. C. and Sumichrast, R. T., 2001, CF-GGA: A grouping genetic algorithm for the cell
formation problem, International Journal of Production Research, 39(16), 3651-3669.
Chandrasekharan, M. P. and Rajagopalan, R., 1989, GROUPABILITY: An analysis of the
properties of binary data matrices for group technology, International Journal of
Production Research, 27(6), 1035-1052.
Cheng, C. H., Gupta, Y. P., Lee, W. H. and Wong, K. F., 1998, TSP-based heuristic for forming
machine groups and part families, International Journal of Production Research, 36(5),
1325-1337.
De Lit, P., Falkenauer, E. and Delchambre, A., 2000, Grouping genetic algorithms: An efficient
method to solve the cell formation problem, Mathematics and Computers in Simulation,
51(3-4), 257-271.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
References
Dimopoulos, C. and Zalzala, A. M. S., 2000, Recent developments in evolutionary
computation for manufacturing optimization: Problems, solutions, and comparisons, IEEE
Transactions on Evolutionary Computation, 4(2), 93-113.
Falkenauer, E., 1998, Genetic Algorithms and Grouping Problems (New York: John Wiley &
Sons).
Gallagher, C. C. and Knight, W. A., 1973, Group Technology (London: Gutterworth).
Gallagher, C. C. and Knight, W. A., 1986, Group Technology Production Methods in
Manufacture (New York: Wiley).
Hyer, N. L. and Wemmerlov, U., 1984, Group Technology and Productivity, Harvard Business
Review, 62(4), 140-149.
King, J. R. and Nakornchai, V., 1982, Machine-Component Group Formation in Group
Technology - Review and Extension, International Journal of Production Research, 20(2),
117-133.
Kumar, C. S. and Chandrasekharan, M. P., 1990, Grouping Efficacy - a Quantitative Criterion
for Goodness of Block Diagonal Forms of Binary Matrices in Group Technology,
International Journal of Production Research, 28(2), 233-243.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne
References
Srinivasan, G. and Narendran, T. T., 1991, GRAFICS. A nonhierarchical clustering algorithm
for group technology, International Journal of Production Research, 29(3), 463-478.
Venugopal, V. and Narendran, T. T., 1992, Genetic algorithm approach to the machinecomponent grouping problem with multiple objectives, Computers & Industrial
Engineering, 22(4), 469-480.
Wemmerlov, U. and Hyer, N. L., 1989, Cellular manufacturing in the US industry: a survey of
users, International Journal of Production Research, 27(9), 1511-1530.
Wu, Y., 1999, Computer aided design of cellular manufacturing layout, Ph.D. Thesis, School
of Engineering and Applied Science, University of Durham.
Yasuda, K., Hu, L. and Yin, Y., 2005, A grouping genetic algorithm for the multi-objective cell
formation problem, International Journal of Production Research, 43(4), 829-853.
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Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne