1. 1 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Teerawut Tunnukij Christian Hicks

2. 2 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Road Map Facilities layout design Start GT/CM Clustering methods Benefits of GT/CM to facilities layout design General problems of clustering methods Suitable methods for the solutions GAs Components of GAs Problems of the classical GAs for solving the cell formation problem GGAs Developed GGA General structure & components of the developed GGA Comparisons & performance Performance & benefits of the proposed GGA Goal

3. 3 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Select machines for each operation and specify operation sequences The facilities layout design Layout Design Transportation System Design Transportation System Design Job Assignment Cell Formation Group machines into cells Assign cells within plants and machines within cells Design aisle structure and select material handling equipment

4. 4 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Group Technology & Cellular Manufacturing Clustering Methods Manufacturing cells Manufacturing cells have been used for identifying 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).

5. 5 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Manufacturing Layout Process (Functional) LayoutGroup (Cellular) Layout Like resources placed together Resources to produce like products placed together TTT M M M T M SGCG SG DD D D TTTCG TTTSG MM DDD MM DDD A cluster or cell

6. 6 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Cellular Manufacturing (CM) Cellular Manufacturing Cellular Manufacturing The parts that have similar processing requirements and/or geometrical shapes. The machines that are required for the manufacture of each part family. Determines & Groups Part Familie s Machin e Cells A manufacturing cell is a cluster of dissimilar machines placed in close proximity and dedicated to the manufacture of a family of parts.

7. 7 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The benefits of CM Cellular Manufacturing Cellular Manufacturing Main benefits Reduced throughput time Reduced work in progress Improved material flows Others Reduced inventory Improved use of space Improved team work Reduced waste Increased flexibility Reduced Manufacturing Costs

8. 8 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Clustering Methods A large number of clustering methods have been developed Part family grouping Machine grouping Machine-part grouping Can be classified into Form part families and then group machines into cells. Form machine cells based upon similarities in part routing and then allocate parts to cells. Form part families and machine cells simultaneously.

9. 9 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne Clustering Methods Part family groupingMachine grouping Machine-part grouping Classification & Coding Similarity coefficient- based Methods Graph theoretic Machine-Part incidence matrix-based Methods Most of these methods have exploited the machine-part matrix as the initial information to identify potential manufacturing cells. Mathematical Programming- based Methods Heuristic Methods Meta-heuristic Methods

10. 10 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne A machine-part incidence matrix (a) the original matrix(b) a rearranged matrix into block-diagonal forms Exceptional elements Parts Machines

11. 11 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 non- deterministic polynomial (NP) complete problem. Meta-heuristic methods Meta-heuristic methods Good methods for the solution SA, TS, GAs

12. 12 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’.

13. 13 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne GAs: The main components GAs 1. Genetic representation 2. Method for generating the initial population 3. Evaluation function 4. Reproduction selection scheme 5. Genetic operators 6. Mechanism for creating successive generations 7. Stopping Criteria 8. GA parameter settings

14. 14 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. 6 parts (or machines) Cell number Chromosome: Cell 1: 1,2,6 Cell 2: 3,5 Cell 3: 4 The general chromosome representation A potential solution

15. 15 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) All chromosomes represent the same solution This repetition problem increases the size of the search space; reduces the effectiveness of the GAs.

16. 16 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.

17. 17 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: The general structure Start Encode Genes Generate Population Genetic Operation Parent 1 Crossover operation Parent 2 Offspring 1 Offspring 2 Parent 1 Offspring 1 Mutation operation Chromosome 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 Evaluate Fitness Grouping efficacy Roulette Wheel Stop Terminate? Number of generation Yes No Chromosome selection Create population for the next generation Randomly combine genes with a repair process Integer representing a cell number Chromosome Random selection 12 3 4 4.1 5 6 7

18. 18 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Genetic representation 6 parts Cell number 4 machines Cell section Chromosome: Cell 1: p1,p2,p6; m3 Cell 2: p3,p5;m2,m4 Cell 3: p4;m1

19. 19 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Generating the initial population The initial population of chromosomes is generated randomly with a repair process that rectifies empty cells. Each cell must contain at least one part and one machine. Wichmann and Hill’s seed-based random number generator was adapted for generating random numbers in the developed GGA, with a very large period of 2.78x10 13. The developed GGA can solve the CFP without the predetermination of the No. of manufacturing cells and the No. of machines within the cell.

20. 20 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Genetic operators Falknauer’s crossover Injection point Select crossover points Injection Remove the empty cell Relocate unassigned components by the replacement heuristic

21. 21 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Genetic operators Elimination mutation Eliminating cell Select a cell number Elimination Relocate unassigned components by the replacement heuristic

22. 22 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Repair process Check & remove empty cells Check the number of cells Replace duplicate cell numbers Relocate unassigned components Each cell must contain at least one part & one machine 2≤C≤min(M-1,P-1) C<2: a new cell no. will be inserted to the cell section; C>min(M-1,P-1): a cell(s) will be randomly selected and eliminated. The duplicate cell no. is replaced with a new cell no. The replacement heuristic utilises the information in the given machine-part matrix to place: unassigned parts in the existing cell that contains the most machine(s) it needs by examining the column j; unassigned machines in the existing cell that contains the most part(s) that needs it by examining the row i. 1 2 3 4

23. 23 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The developed GGA: Evaluation criteria Grouping efficacy ( г ) where e the total number of operations (number of 1s in the matrix); e 0 the number of 1s in the off- diagonal blocks; e v the number of voids in the diagonal blocks. The best solution is to minimise a number of voids in the diagonal blocks which indicate unutilised machines in each cell; a number of exceptions (ones outside of the diagonal blocks) which represent inter-cell flows.

24. 24 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The analysis of performance A simple CFP (a) The 5x8 original matrix (b) The 5x8 matrix after clustered the performance of the GGA proposed by Yasuda, et al. (2005) the performance of the developed GGA

25. 25 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The analysis of performance Comparisons of five clustering algorithms CR1-CR7 obtained from Chandrasekharan and Rajagopalan (1989) KN1 obtained from King and Nakornchai (1982)

26. 26 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne The analysis of performance Grouping efficacy

27. 27 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.

28. 28 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.

29. 29 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne

30. 30 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.

31. 31 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.

32. 32 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 machine- component 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.