Teerawut Tunnukij Christian Hicks 1 Mr T Tunnukij

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 GAs Benefits of GT/CM to facilities layout design Facilities layout design GGAs 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 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). 4 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 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 D D M SG Resources to produce like products placed together 5 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 7 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. 8 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. 9 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne

A machine-part incidence matrix Exceptional elements Parts Machines Parts (a) the original matrix (b) a rearranged matrix into block-diagonal forms 10 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 13 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) A potential solution Cell number Chromosome: Cell 1: 1, 2, 6 Cell 2: 3, 5 Cell 3: 4 14 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. 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 17 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne

The analysis of performance A simple CFP the performance of the GGA proposed by Yasuda, et al. (2005) (a) The 5 x 8 original matrix the performance of the developed GGA (b) The 5 x 8 matrix after clustered 24 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne

The analysis of performance Comparisons of five clustering algorithms • CR 1 -CR 7 obtained from Chandrasekharan and Rajagopalan (1989) • KN 1 obtained from King and Nakornchai (1982) 25 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne

Grouping efficacy The analysis of performance 26 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. 28 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. 3651 -3669 , (16)39 , 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. 1035 -1052 , (6)27 , Cheng, C. H. , Gupta, Y. P. , Lee, W. H. and Wong, K. F. , 1998, TSP-based heuristic forming machine groups and part families, International Journal of Production Research , . 1325 -1337 , (5)36 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. 30 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 , (2)20 , . 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. 233 -243 , (2)28 , 31 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. 463 -478 , (3)29 , 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. 829 -853 , (4)43 , 32 Mr T. Tunnukij & Dr. C. Hicks, University of Newcastle upon Tyne