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A hybrid clonal algorithm for the cell formation problem with variant number of cells

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Abstract

Cellular manufacturing is an important application of the Group Technology that has been used in several real-world applications such as the electronics industry, offices, structural fabrication, service industries, and hospitals. The manufacturing cell formation problem is considered the first issue faced in designing cellular manufacturing systems in order to overcome difficulties related to multi-product and batch-production systems. The aim is to minimize the inter-cell movements of the parts and maximize the use of the machines. In this paper, a new approach based on the clonal selection algorithm is proposed for solving the problem where the number of cells is not fixed a priori. The approach integrates a local search mechanism to intensify the search of the solutions. To evaluate the effectiveness of the proposed algorithm, a set of 40 benchmark problems is used; the results are then compared to other methods recently developed. The results show that the proposed algorithm performs very well on all test problems since it can reach the best-known solution of 39 benchmark problems (97.5%).

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References

  1. Wemmerlov U, Hyer NL (1986) Procedures for the part family/machine group identification problem in cellular manufacturing. J Oper Manag 6:125–147

    Article  Google Scholar 

  2. Wemmerlov U, Hyer NL (1989) Cellular manufacturing in the US industry: a survey of users. Int J Prod Res 27:1511–1530

    Article  Google Scholar 

  3. Dinis-Carvalho J, Alves AC, Sousa RM (2014) Moving from job-shop to production cells without losing flexibility: a case study from the wooden frames industry. S Afr J Ind Eng 25:212–225

    Google Scholar 

  4. Hung K-T, Maleki H (2014) Applying group technology to the forging industry. Prod Plan Control Manag Oper 25:134–148

    Article  Google Scholar 

  5. Johnson DJ, Wemmerlv U (2004) Why does cell implementation stop? Factors influencing cell penetration in manufacturing plants. Prod Oper Manag 13:272–289

    Article  Google Scholar 

  6. Pattanaik LN, Sharma BP (2008) Implementing lean manufacturing with cellular layout: a case study. Int J Adv Manuf Technol 42:772–779

    Article  Google Scholar 

  7. Shayan E, Sobhanallahi A (2002) Productivity gains by cellular manufacturing. Prod Plan Control 13:507–516

    Article  Google Scholar 

  8. Levasseur GA, Helms MM, Zink AA (1995) A conversion from a functional to a cellular manufacturing layout at Steward, Inc. Prod Invent Manag J 36:37–42

    Google Scholar 

  9. Landsbergis PA, Cahill J, Schnall P (1999) The impact of lean production and related new systems of work organization on worker health. J Occup Health Psychol 4:108–130

    Article  Google Scholar 

  10. Dimopoulos C, Zalzala AM (2000) Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans Evolut Comput 4:93–113

    Article  Google Scholar 

  11. Goncalves J, Resende M (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47:247–273

    Article  Google Scholar 

  12. James TL, Brown EC, Keeling KB (2007) A hybrid grouping genetic algorithm for the cell formation problem. Comput Oper Res 34:2059–2079

    Article  MATH  Google Scholar 

  13. Diaz JA, Luna D, Luna R (2012) A GRASP heuristic for the manufacturing cell. Top 20:679–706

    Article  MathSciNet  MATH  Google Scholar 

  14. Solimanpur M, Elmi A (2013) A tabu search approach for cell scheduling problem with makespan criterion. Int J Prod Econ 141:639–645

    Article  Google Scholar 

  15. Husseinzadeh Kashan A, Karimi B, Noktehdan A (2014) A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem. Int J Adv Manuf Technol 73:1543–1556

    Article  Google Scholar 

  16. Ying KC, Lin SW, Lu CC (2011) Cell formation using a simulated annealing algorithm with variable neighbourhood. Eur J Ind Eng 5:22–42

    Article  Google Scholar 

  17. Elbenani B, Ferland JA, Bellemare J (2012) Genetic algorithm and large neighbourhood search to solve the cell formation problem. Expert Syst Appl 39:2408–2414

    Article  Google Scholar 

  18. Noktehdan A, Seyedhosseini S, Saidi-Mehrabad M (2015) A metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy. Int J Adv Manuf Technol 82:25–37. doi:10.1007/s00170-015-7052-z

    Article  Google Scholar 

  19. Karoum B, Elbenani B, El Imrani AA (2016) Clonal selection algorithm for the cell formation problem. In: El Oualkadi A et al (eds) Proceedings of the Mediterranean conference on information and communication technologies 2015, Lecture Notes in Electrical Engineering, vol 380, pp 319–326

  20. Kumar CS, Chandrasekharan MP (1990) Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28:233–243

    Article  Google Scholar 

  21. De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput 6:239–251

    Article  Google Scholar 

  22. Brown EC, Sumichrast RT (2001) CF-GGA: a grouping genetic algorithm for the cell formation problem. Int J Prod Res 39:3651–3669

    Article  MATH  Google Scholar 

  23. King JR, Nakornchai V (1982) Machine-component group formation in group technology: review and extension. Int J Prod Res 20:117–133

    Article  Google Scholar 

  24. Waghodekar PH, Sahu S (1984) Machine-component cell formation in group technology: MACE. Int J Prod Res 22:937–948

    Article  Google Scholar 

  25. Seifoddini H (1989) A note on the similarity coefficient method and the problem of improper machine assignment in group technology applications. Int J Prod Res 27:1161–1165

    Article  Google Scholar 

  26. Kusiak A, Cho M (1992) Similarity coefficient algorithms for solving the group technology problem. J Manuf Syst 30:2633–2646

    Google Scholar 

  27. Kusiak A, Chow WS (1987) Efficient solving of the group technology problem. J Manuf Syst 6:117–124

    Article  Google Scholar 

  28. Boctor FF (1991) A linear formulation of the machine-part cell formation problem. Int J Prod Res 29:343–356

    Article  Google Scholar 

  29. Seifoddini H, Wolfe PM (1986) Application of the similarity coefficient method in group technology. IIE Trans 18:271–277

    Article  Google Scholar 

  30. Chandrasekharan MP, Rajagopalan R (1986) MODROC: an extension of rank order clustering for group technology. Int J Prod Res 24:1221–1233

    Article  Google Scholar 

  31. Chandrasekharan MP, Rajagopalan R (1986) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24:451–463

    Article  MATH  Google Scholar 

  32. Mosier C, Taube L (1985) The facets of group technology and their impacts on implementation—a state-of-the-art survey. Omega 13:381–391

    Article  Google Scholar 

  33. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1:65–75

    Article  Google Scholar 

  34. Asktn RG, Subramantan SP (1987) A cost-based heuristic for group technology configuration. Int J Prod Res 25:101–113

    Article  Google Scholar 

  35. Stanfel LE (1985) Machine clustering for economic production. Eng Costs Prod Econ 9:73–81

    Article  Google Scholar 

  36. McCormick WT (1972) Problem decomposition and data reorganization by a clustering technique. Oper Res 20:993–1009

    Article  MATH  Google Scholar 

  37. Srinivasan G, Narendran TT, Mahadevan B (1990) An assignment model for the part-families problem in group technology. Int J Prod Res 28:145–152

    Article  Google Scholar 

  38. King JR (1980) Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. Int J Prod Res 18:213–232

    Article  Google Scholar 

  39. Carrie AS (1973) Numerical taxonomy applied to group technology and plant layout. Int J Prod Res 11:399–416

    Article  Google Scholar 

  40. Mosier C, Taube L (1985) Weighted similarity measure heuristics for the group technology machine clustering problem. Omega 13:577–579

    Article  Google Scholar 

  41. Kumar KR, Kusiak A, Vannelli A (1986) Grouping of parts and components in flexible manufacturing systems. Eur J Oper Res 24:387–397

    Article  MATH  Google Scholar 

  42. Boe WJ, Cheng CH (1991) A close neighbour algorithm for designing cellular manufacturing systems. Int J Prod Res 29:2097–2116

    Article  MATH  Google Scholar 

  43. Chandrasekharan MP, Rajagopalan R (1989) GROUPABIL1TY: an analysis of the properties of binary data matrices for group technology. Int J Prod Res 27:1035–1052

    Article  Google Scholar 

  44. Kumar KR, Vannelli A (1987) Strategic subcontracting for efficient disaggregated manufacturing. Int J Prod Res 25:1715–1728

    Article  Google Scholar 

  45. Chandrasekharan MP, Rajagopalan R (1987) ZODIAC—an algorithm for concurrent formation of part-families and machine-cells. Int J Prod Res 25:835–850

    Article  MATH  Google Scholar 

  46. Noktehdan A, Karimi B, Husseinzadeh KA (2010) A differential evolution algorithm for the manufacturing cell formation problem using group based operators. Expert Syst Appl 37:4822–4829

    Article  Google Scholar 

  47. Thanh LT, Ferland JA, Elbenani B, Dinh Thuc N, Hien Nguyen V (2015) A computational study of hybrid approaches of metaheuristic algorithms for the cell formation problem. J Oper Res Soc 67:20–36

    Article  Google Scholar 

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Authors and Affiliations

  1. Research Computer Science Laboratory (LRI), Faculty of Science, Mohammed V University of Rabat, B.P. 1014, Rabat, Morocco

    Bouchra Karoum & Bouazza Elbenani

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  1. Bouchra Karoum
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  2. Bouazza Elbenani
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Correspondence to Bouchra Karoum.

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Karoum, B., Elbenani, B. A hybrid clonal algorithm for the cell formation problem with variant number of cells. Prod. Eng. Res. Devel. 11, 19–28 (2017). https://doi.org/10.1007/s11740-016-0706-3

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