Skip to main content

Advertisement

SpringerLink
Log in

A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The manufacturing cell formation problem, with the aim of grouping parts into families and machines into cells, is considered with the objective of maximizing grouping efficacy. A new solution approach based on the particle swarm optimization (PSO) algorithm is presented for the problem. Unlike the original PSO algorithm which works with arithmetic operators and scalars, the new algorithm uses group-based operators, in place of arithmetic operators, in the body of the updating equations analogous to those of the classical PSO equations (given the fact that the cell formation problem is essentially a grouping problem, all operators in the new algorithm work with constructed cells (groups) rather than parts/machines (objects), isolatedly). We benchmark a set of 40 test problem instances from previous researches and do comparisons between the new algorithm and existing algorithms. We also compare the performance of our algorithm when it is hybridized with a local search module. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution’s quality presented in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. De Lit P, Falkenauer E, Delchambre A (2000) Grouping genetic algorithms: an efficient method to solve the cell formation problem. Math Comput Simul 51:257–271

    Article  Google Scholar 

  2. Brown EC, Sumichrast R (2001) CF-GGA: a grouping genetic algorithm for the cell formation problem. Int J Prod Res 36:3651–3669

    Article  Google Scholar 

  3. Vin E, De Lit P, Delchamber A (2005) A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings. J Intell Manuf 16:189–205

    Article  Google Scholar 

  4. 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 

  5. Falkenauer E (1994) New representation and operators for GAs applied to grouping problems. Evol Comput 2:123–144

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Wu TH, Chang CC, Chung SH (2008) A simulated annealing algorithm to manufacturing cell formation problems. Expert Syst Appl 34:1609–1617

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Selim HM, Askin RG, Vakharia AJ (1998) Cell formation in group technology: evaluation and directions for future research. Comput Ind Eng 34:3–20

    Article  Google Scholar 

  11. 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 

  12. Krushinsky D, Goldengorin B (2012) An exact model for cell formation in group technology. Comput Manag Sci 9:323–338

    MATH  MathSciNet  Google Scholar 

  13. Goldengorin B, Krushinsky D, Slomp J (2012) Flexible PMP approach for large size cell formation. Oper Res 60:1157–1166

    Article  MATH  MathSciNet  Google Scholar 

  14. Ghosh T, Sengupta S, Chattopadhyay M, Dan KP (2011) Meta-heuristics in cellular manufacturing: a state-of-art review. Int J Ind Eng Comput 2:87–122

    Google Scholar 

  15. Sun D, Lin L, Batta R (1995) Cell formation using tabu search. Comput Ind Eng 28:485–494

    Article  Google Scholar 

  16. Lei D, Wu Z (2005) Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int J Prod Res 19:4035–4047

    Article  Google Scholar 

  17. Pailla A, Trindade A, Parada V, Ochi L (2010) A numerical comparison between simulated annealing and evolutionary approaches to the cell formation problem. Expert Syst Appl 37:5476–5483

    Article  Google Scholar 

  18. Stawowy A (2006) Evolutionary strategy for manufacturing cell design. Omega, Int J Manag Sci 34:1–18

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Cheng C, Gupa Y, Lee WA (1998) TSP-based heuristic for forming machine groups and part families. Int J Prod Res 36:1325–1337

    Article  MATH  Google Scholar 

  21. Boulif M, Atif K (2006) A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem. Comput Oper Res 33:2219–2245

    Article  MATH  Google Scholar 

  22. Lei D, Wu Z (2006) Tabu search for multiple-criteria manufacturing cell design. Int J Adv Manuf Technol 28:950–956

    Article  Google Scholar 

  23. Wu TH, Chung SH, Chang CC (2009) Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings. Expert Syst Appl 36:3652–3661

    Article  Google Scholar 

  24. Ateme-Nguema B, Dao T (2007) Optimization of cellular manufacturing systems design using the hybrid approach based on the ant colony and tabu search techniques. In: Proceedings of the IEEE IEEM pages 668–673

  25. Ateme-Nguema B, Dao T (2009) Quantized Hopfield networks and tabu search for manufacturing cell formation problems. Int J Prod Econ 121:88–98

    Article  Google Scholar 

  26. Anvari M, Mehrabad M, Barzinpour F (2010) Machine-part cell formation using a hybrid particle swarm optimization. Int J Adv Manuf Technol 47:745–754

    Article  Google Scholar 

  27. Wu TH, Chung SH, Chang CC (2010) A water flow-like algorithm for manufacturing cell formation problems. Eur J Oper Res 205:346–360

    Article  MATH  Google Scholar 

  28. Solimanpur M, Saeedi S, Mahdavi I (2010) Solving cell formation problem in cellular manufacturing using ant-colony-based optimization. Int J Adv Manuf Technol 50:1135–1144

    Article  Google Scholar 

  29. Prabhaharan G, Muruganandam A, Asokan P, Girish BS (2005) Machine cell formation for cellular manufacturing systems using an ant colony system approach. Int J Adv Manuf Technol 25:1013–1019

    Article  Google Scholar 

  30. Batsyn M, Bychkov I, Goldengorin B, Pardalos P, Sukhov P (2013) Pattern-based heuristic for the cell formation problem in Group Technology. In: Goldengorin B et al (eds) Models, algorithms, and technologies for network analysis. Springer Proceedings in Mathematics & Statistics 32:11–50

  31. Kennedy J, Eberhard RC (1995) Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ, USA, 1942–1948

  32. Kennedy J, Eberhard RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of IEEE Conference on Systems, man, and cybernetics. Piscataway, NJ, USA, 4104–4109

  33. Laskari EC, Parsopoulos KE, Vrahatis MN (2002) Particle swarm optimization for integer programming. In: Proceedings of the IEEE 2002 Congress on evolutionary computation. Honolulu (HI) 1582–1587

  34. Heragu SS (1997) Facility design. PWS Publishing Company, Boston

    Google Scholar 

  35. Husseinzadeh Kashan A, Karimi B, Jolai F (2006) Effective hybrid genetic algorithm for minimizing makespan on a single-batch-processing machine with non-identical job sizes. Int J Prod Res 44:2337–2360

    Article  Google Scholar 

  36. 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 

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

    Article  Google Scholar 

  38. Seifiddini 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 

  39. Kusiak A, Cho M (1992) Similarity coefficient algorithm for solving the group technology problem. Int J Prod Res 30:2633–263346

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Seifiddini H, Wolf PM (1986) Application of the similarity coefficient method in group technology. IIIE Trans 18:271–277

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  44. Mosier CT, Taube L (1985) The facets of group technology and their impact on implementation. OMEGA 13:381–391

    Article  Google Scholar 

  45. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacturing. J Manuf Syst 1:65–74

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. McCormick WT, Schweitzer PJ, White TW (1972) Problem decomposition and data reorganization by a clustering technique. Oper Res 20:993–1009

    Article  MATH  Google Scholar 

  49. Srinivasan G, Narendran T, Mahadevan B (1990) An assignment model for the Part-families problem in group technology. Int J Prod Res 145–152

  50. 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 

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

    Article  Google Scholar 

  52. Mosier CT, Taube L (1985) Weighted similarity measure heuristics for the group technology machine clustering problem. OMEGA 13:577–583

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  56. Kumar KR, Vannelli A (1983) Strategic subcontracting for efficient disaggregated manufacturing. Int J Prod Res 25:1715–1728

    Google Scholar 

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

    Article  Google Scholar 

  58. 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 

  59. Onwubulo GC, Mutingi M (2001) A genetic algorithm approach to cellular manufacturing systems. Comput Ind Eng 39:125–144

    Article  Google Scholar 

  60. Srinivasan G, Narendran T (1991) GRAFICS—a nonhierarchical clustering-algorithm for group technology. Int J Prod Res 29:463–478

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Husseinzadeh Kashan

  2. Department of Industrial Engineering, Amirkabir University of Technology, P.O. box: 15875-4413, Tehran, Iran

    Behrooz Karimi

  3. Department of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Azadeh Noktehdan

Authors
  1. Ali Husseinzadeh Kashan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behrooz Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azadeh Noktehdan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrooz Karimi.

Additional information

Please contact the authors for the source codes if any reader is interested in them.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Husseinzadeh Kashan, A., Karimi, B. & Noktehdan, A. A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem. Int J Adv Manuf Technol 73, 1543–1556 (2014). https://doi.org/10.1007/s00170-014-5906-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-5906-4

Keywords

Search

Navigation