Skip to main content

Advertisement

Springer Nature Link
Log in

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Soleymanpour M, Vrat P, Shankar R (2002) A transiently chaotic neural network approach to the design of cellular manufacturing. Int J Prod Res 40(10):2225–2244

    Article  MATH  Google Scholar 

  2. Wemmerlöv U, Hyer NL (1989) Cellular manufacturing in the US industry: a survey of users. Int J Prod Res 27(9):1511–1530

    Article  Google Scholar 

  3. Lei D, Wu Z (2005) Tabu search approach based on a similarity coefficient for cell formation in generalized group technology. Int J Prod Res 43(19):4035–4047

    Article  MATH  Google Scholar 

  4. Ballakur A, Steudel HJ (1987) A within-cell utilization based heuristic for designing cellular manufacturing systems. Int J Prod Res 25(5):639–665

    Article  Google Scholar 

  5. Papaioannou G, Wilson JM (2010) The evolution of cell formation problem methodologies based on recent studies (1997–2008): review and directions for future research. Eur J Oper Res 206(3):509–521

    Article  MATH  Google Scholar 

  6. 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(7):4822–4829

    Article  Google Scholar 

  7. Mirzapour Al-e-hashem SMJ, Aryanezhad MB, Jabbarzadeh A (2011) A new approach to solve a mixed-model assembly line with a bypass subline sequencing problem. Int J Adv Manuf Technol 52(9–12):1053–1066

  8. Aryanezhad M.-B., Naini, SGJ, Jabbarzadeh A (2011) An integrated location inventory model for designing a supply chain network under uncertainty. Life Science Journal-acta Zhengzhou University Overseas Edition 8(4):670–679

  9. Potočnik P, Berlec T, Starbek M, Govekar E (2013) Self-organizing neural network-based clustering and organization of production cells. Neural Comput and Applic 22(1):113–124. doi:10.1007/s00521-012-0938-x

    Article  Google Scholar 

  10. Liao TW, Chen L (1993) An evaluation of ART1 neural models for GT part family and machine cell forming. J Manuf Syst 12(4):282–290

    Article  Google Scholar 

  11. Dagli C, Huggahalli R (1995) Machine-part family formation with the adaptive resonance theory paradigm. Int J Prod Res 33(4):893–913

    Article  MATH  Google Scholar 

  12. Burke L, Kamal S (1995) Neural networks and the part family/machine group formation problem in cellular manufacturing: a framework using fuzzy ART. J Manuf Syst 14(3):148–159

    Article  Google Scholar 

  13. Chen D-S, Chen H-C, Park J-M (1996) An improved ART neural net for machine cell formation. J Mater Process Technol 61(1):1–6

    Article  Google Scholar 

  14. Vrat P, All AAM (1995) I design of cellular manufacturing systems: i Hopfield neural network approach. Recent Trends In Applied Systems Research:467

  15. Zolfagha Ri S, Liang M A. Hopfield neural network approach to machine grouping problem. In: 14th Industrial Engineering Research Conference, Nashville, TN, USA, 1995. pp 542–549

  16. Zolfagha Ri S, Liang M (1997) An objective-guided ortho-synapse Hopfield network approach to machine grouping problems. Int J Prod Res 35(10):2773–2792

    Article  MATH  Google Scholar 

  17. Onwubolu GC (1999) Design of parts for cellular manufacturing using neural network-based approach. J Intell Manuf 10(3–4):251–265

    Article  Google Scholar 

  18. Lozano S, Canca D, Guerrero F, Garcia J (2001) Machine grouping using sequence-based similarity coefficients and neural networks. Robot Comput Integr Manuf 17(5):399–404

    Article  Google Scholar 

  19. Guerrero F, Lozano S, Smith KA, Canca D, Kwok T (2002) Manufacturing cell formation using a new self-organizing neural network. Comput Ind Eng 42(2):377–382

    Article  Google Scholar 

  20. Venkumar P, Haq AN (2005) Manufacturing cell formation using modified ART1 networks. Int J Adv Manuf Technol 26(7–8):909–916

    Article  Google Scholar 

  21. Venkumar P, Haq AN (2006) Complete and fractional cell formation using Kohonen self-organizing map networks in a cellular manufacturing system. Int J Prod Res 44(20):4257–4271

    Article  Google Scholar 

  22. Yang M-S, Yang J-H (2008) Machine-part cell formation in group technology using a modified ART1 method. Eur J Oper Res 188(1):140–152

    Article  MATH  Google Scholar 

  23. Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications 2008:3

    Google Scholar 

  24. Poli R (2007) An analysis of publications on particle swarm optimization applications. Department of Computer Science, University of Essex, Essex, Colchester

  25. Sheikhan M, Garoucy S (2013) Substitution of G.728 vocoder’s codebook search module with SOM array trained by PSO-optimized supervised algorithm. Neural Comput and Applic 23(7–8):2309–2321. doi:10.1007/s00521-012-1183-z

    Article  Google Scholar 

  26. Andrés C, Lozano S (2006) A particle swarm optimization algorithm for part–machine grouping. Robot Comput Integr Manuf 22(5):468–474

    Article  Google Scholar 

  27. Duran O, Rodriguez N, Consalter LA A PSO-based clustering algorithm for manufacturing cell design. In: Knowledge discovery and data mining, 2008. WKDD 2008. First International Workshop on, 2008. IEEE, USA, pp 72–75

  28. Wu T-H, Chang C-C, Chung S-H (2008) A simulated annealing algorithm for manufacturing cell formation problems. Expert Syst Appl 34(3):1609–1617

    Article  Google Scholar 

  29. Chandrasekharan M, Rajagopalan R (1989) GROUPABIL1TY: an analysis of the properties of binary data matrices for group technology. Int J Prod Res 27(6):1035–1052

    Article  Google Scholar 

  30. Suresh Kumar C, Chandrasekharan M (1990) Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28(2):233–243

    Article  Google Scholar 

  31. Cheng CH, Gupta Y, Lee W, Wong K (1998) A TSP-based heuristic for forming machine groups and part families. Int J Prod Res 36(5):1325–1337

    Article  MATH  Google Scholar 

  32. Chung S-H, Wu T-H, Chang C-C (2011) An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Comput Ind Eng 60(1):7–15

    Article  Google Scholar 

  33. Li X, Baki M, Aneja YP (2010) An ant colony optimization metaheuristic for machine–part cell formation problems. Comput Oper Res 37(12):2071–2081

    Article  MATH  Google Scholar 

  34. Hakimi-Asiabar M, Ghodsypour SH, Kerachian R (2009) Multi-objective genetic local search algorithm using Kohonen’s neural map. Comput Ind Eng 56(4):1566–1576

    Article  Google Scholar 

  35. Jabbarzadeh A, Jalali Naini SG, Davoudpour H, Azad N (2012) Designing a supply chain network under the risk of disruptions. Math Probl Eng 2012

  36. Jabbarzadeh A, Fahimnia B, Seuring S (2014) Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transportation Research Part E: Logistics and Transportation. Review 70:225–244

    Article  Google Scholar 

  37. Zokaee S, Jabbarzadeh A, Fahimnia B, Sadjadi SJ (2014) Robust supply chain network design: an optimization model with real world application. Ann Oper Res:1–30

  38. Fahimnia B, Jabbarzadeh A, Ghavamifar A, Bell M (2017) Supply chain design for efficient and effective blood supply in disasters. Int J Prod Econ 183:700–709

  39. Jabbarzadeh A, Fahimnia B, Sheu J-B (2017) An enhanced robustness approach for managing supply and demand uncertainties. Int J Prod Econ 183:620–631

  40. Jabbarzadeh A, Fahimnia B, Sheu J-B, Moghadam HS (2016) Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp Res B Methodol 94:121–149

    Article  Google Scholar 

  41. Fahimnia B, Jabbarzadeh A (2016) Marrying supply chain sustainability and resilience: a match made in heaven. Transportation Research Part E: Logistics and Transportation Review 91:306–324

    Article  Google Scholar 

  42. Shishebori D, Jabalameli MS, Jabbarzadeh A (2013). Facility location-network design problem: reliability and investment budget constraint. J Urban Plann Dev 140, 04014005

  43. Diabat A, Dehghani E, Jabbarzadeh A (2017) Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. J Manuf Syst 43:139–149

  44. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  45. Liu B (1999) Uncertain programming a Wiley-Interscience publication, New York

  46. Zimmermann H-J (2001) Fuzzy set theory—and its applications. Springer Science and Business Media, Berlin

    Book  Google Scholar 

  47. Safaei N, Saidi-Mehrabad M, Tavakkoli-Moghaddam R, Sassani F (2008) A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets Syst 159(2):215–236

    Article  MathSciNet  MATH  Google Scholar 

  48. Tavakkoli-Moghaddam R, Safaei N, Babakhani M (2005) Solving a dynamic cell formation problem with machine cost and alternative process plan by memetic algorithms. In: Stochastic algorithms: foundations and applications. Springer, Berlin, pp 213–227

    Chapter  MATH  Google Scholar 

  49. Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159(2):193–214

    Article  MathSciNet  MATH  Google Scholar 

  50. Paydar MM, Saidi-Mehrabad M (2015) Revised multi-choice goal programming for integrated supply chain design and dynamic virtual cell formation with fuzzy parameters. Int J Comput Integr Manuf 28(3):251–265

    Article  Google Scholar 

  51. Arikan F, Güngör Z (2005) A parametric model for cell formation and exceptional elements’ problems with fuzzy parameters. J Intell Manuf 16(1):103–114

    Article  Google Scholar 

  52. Barua A, Mudunuri LS, Kosheleva O (2014) Why trapezoidal and triangular membership functions work so well: towards a theoretical explanation. Journal of Uncertain Systems 8(3):164–168

  53. Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci 36(1):59–83

    Article  MathSciNet  MATH  Google Scholar 

  54. Ross TJ (2016) Fuzzy logic with engineering applications. John Wiley & Sons, New Mexico

  55. Kennedy J, Eberhart R Particle swarm optimization. In: Proceedings of IEEE international conference on. neural networks, 1995. Perth, Australia, pp 1942–1948

  56. Kirkpatrick S Jr, DG VMP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  57. Shafer S, Rogers D (1993) Similarity and distance measures for cellular manufacturing. Part I. A survey. Int J Prod Res 31(5):1133–1142

    Article  Google Scholar 

  58. Kohonen T (2001) Self-organizing maps, vol 30. Springer, Berlin

    Book  MATH  Google Scholar 

  59. P Chandrasekharan M, Rajagopalan R (1986) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24(2):451–463

    Article  MATH  Google Scholar 

  60. Mosier C, Taube L (1985) Weighted similarity measure heuristics for the group technology machine clustering problem. Omega 13(6):577–579. doi:10.1016/0305-0483(85)90046-5

    Article  Google Scholar 

  61. King JR, Nakornchai V (1982) Machine-component group formation in group technology: review and extension. Int J Prod Res 20(2):117–133. doi:10.1080/00207548208947754

    Article  Google Scholar 

  62. Ravi Kumar K, Kusiak A, Vannelli A (1986) Grouping of parts and components in flexible manufacturing systems. Eur J Oper Res 24(3):387–397. doi:10.1016/0377-2217(86)90032-9

    Article  MATH  Google Scholar 

  63. Waghodekar PH, Sahu S (1984) Machine-component cell formation in group technology: MACE. Int J Prod Res 22(6):937–948. doi:10.1080/00207548408942513

    Article  Google Scholar 

  64. Carrie AS (1973) Numerical taxonomy applied to group technology and plant layout. Int J Prod Res 11(4):399–416. doi:10.1080/00207547308929988

    Article  Google Scholar 

  65. 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(7):1161–1165. doi:10.1080/00207548908942614

    Article  Google Scholar 

  66. Boe WJ, Cheng CH (1991) A close neighbour algorithm for designing cellular manufacturing systems. Int J Prod Res 29(10):2097–2116. doi:10.1080/00207549108948069

    Article  MATH  Google Scholar 

  67. Kusiak A, Cho M (1992) Similarity coefficient algorithms for solving the group technology problem. Int J Prod Res 30(11):2633–2646. doi:10.1080/00207549208948181

    Article  Google Scholar 

  68. Chandrasekharan MP, Rajagopalan R (1989) GROUPABIL1TY: an analysis of the properties of binary data matrices for group technology. Int J Prod Res 27(6):1035–1052. doi:10.1080/00207548908942606

    Article  Google Scholar 

  69. Kusiak A, Chow WS (1987) Efficient solving of the group technology problem. J Manuf Syst 6(2):117–124. doi:10.1016/0278-6125(87)90035-5

    Article  Google Scholar 

  70. Boctor FF (1991) A Jinear formulation of the machine-part cell formation problem. Int J Prod Res 29(2):343–356. doi:10.1080/00207549108930075

    Article  Google Scholar 

  71. Seifoddini H, Wolfe PM (1986) Application of the similarity coefficient method in group technology. IIE Trans 18(3):271–277. doi:10.1080/07408178608974704

    Article  Google Scholar 

  72. Chandrasekharan MP, Rajagopalan R (1986) MODROC: an extension of rank order clustering for group technology. Int J Prod Res 24(5):1221–1233. doi:10.1080/00207548608919798

    Article  Google Scholar 

  73. Mosier C, Taube L (1985) The facets of group technology and their impacts on implementation—a state-of-the-art survey. Omega 13(5):381–391. doi:10.1016/0305-0483(85)90066-0

    Article  Google Scholar 

  74. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1(1):65–75. doi:10.1016/S0278-6125(82)80068-X

    Article  Google Scholar 

  75. Ravi Kumar K, Vannelli A (1987) Strategic subcontracting for efficient disaggregated manufacturing. Int J Prod Res 25(12):1715–1728

    Google Scholar 

  76. Stanfel LE (1985) Machine clustering for economic production. Engineering Costs and Production Economics 9(1–3):73–81. doi:10.1016/0167-188X(85)90012-6

    Article  Google Scholar 

  77. McCormick WT, Schweitzer PJ, White TW (1972) Problem decomposition and data reorganization by a clustering technique. Oper Res 20(5):993–1009. doi:10.1287/opre.20.5.993

    Article  MATH  Google Scholar 

  78. Srinl Vasan G, Narendran TT, Mahadevan B (1990) An assignment model for the part-families problem in group technology. Int J Prod Res 28(1):145–152. doi:10.1080/00207549008942689

    Article  Google Scholar 

  79. Wemmerlov U, Johnson DJ (1997) Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int J Prod Res 35(1):29–49

    Article  MATH  Google Scholar 

  80. Singh N, Rajamani D (2012) Cellular manufacturing systems: design, planning and control. Springer Science and Business Media, Berlin

    Google Scholar 

  81. Haykin SS (2009) Neural networks and learning machines, vol 3. Pearson Education, Upper Saddle River

    Google Scholar 

Download references

Acknowledgements

The authors are grateful to the managerial team of the case company for providing the related data for our analysis.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Vahid Mahmoodian, Armin Jabbarzadeh & Farnaz Barzinpour

  2. Department of Industrial Engineering, University of Tabriz, Tabriz, Iran

    Hassan Rezazadeh

Authors
  1. Vahid Mahmoodian
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Armin Jabbarzadeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hassan Rezazadeh
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Farnaz Barzinpour
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Armin Jabbarzadeh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahmoodian, V., Jabbarzadeh, A., Rezazadeh, H. et al. A novel intelligent particle swarm optimization algorithm for solving cell formation problem. Neural Comput & Applic 31 (Suppl 2), 801–815 (2019). https://doi.org/10.1007/s00521-017-3020-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3020-x

Keywords