Neural Computing and Applications

, Volume 31, Supplement 2, pp 801–815 | Cite as

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

  • Vahid Mahmoodian
  • Armin JabbarzadehEmail author
  • Hassan Rezazadeh
  • Farnaz Barzinpour
Original Article


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.


Cellular manufacturing Cell formation problem Neural networks Particle swarm optimization Discrete learning 



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

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 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):2225–2244zbMATHGoogle Scholar
  2. 2.
    Wemmerlöv U, Hyer NL (1989) Cellular manufacturing in the US industry: a survey of users. Int J Prod Res 27(9):1511–1530Google Scholar
  3. 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):4035–4047zbMATHGoogle Scholar
  4. 4.
    Ballakur A, Steudel HJ (1987) A within-cell utilization based heuristic for designing cellular manufacturing systems. Int J Prod Res 25(5):639–665Google Scholar
  5. 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):509–521zbMATHGoogle Scholar
  6. 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):4822–4829Google Scholar
  7. 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):1053–1066Google Scholar
  8. 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):670–679Google Scholar
  9. 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 Google Scholar
  10. 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):282–290Google Scholar
  11. 11.
    Dagli C, Huggahalli R (1995) Machine-part family formation with the adaptive resonance theory paradigm. Int J Prod Res 33(4):893–913zbMATHGoogle Scholar
  12. 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):148–159Google Scholar
  13. 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):1–6Google Scholar
  14. 14.
    Vrat P, All AAM (1995) I design of cellular manufacturing systems: i Hopfield neural network approach. Recent Trends In Applied Systems Research:467Google Scholar
  15. 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 542–549Google Scholar
  16. 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):2773–2792zbMATHGoogle Scholar
  17. 17.
    Onwubolu GC (1999) Design of parts for cellular manufacturing using neural network-based approach. J Intell Manuf 10(3–4):251–265Google Scholar
  18. 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):399–404Google Scholar
  19. 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):377–382Google Scholar
  20. 20.
    Venkumar P, Haq AN (2005) Manufacturing cell formation using modified ART1 networks. Int J Adv Manuf Technol 26(7–8):909–916Google Scholar
  21. 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):4257–4271Google Scholar
  22. 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):140–152zbMATHGoogle Scholar
  23. 23.
    Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications 2008:3Google Scholar
  24. 24.
    Poli R (2007) An analysis of publications on particle swarm optimization applications. Department of Computer Science, University of Essex, Essex, ColchesterGoogle Scholar
  25. 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 Google Scholar
  26. 26.
    Andrés C, Lozano S (2006) A particle swarm optimization algorithm for part–machine grouping. Robot Comput Integr Manuf 22(5):468–474Google Scholar
  27. 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 72–75Google Scholar
  28. 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):1609–1617Google Scholar
  29. 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):1035–1052Google Scholar
  30. 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):233–243Google Scholar
  31. 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):1325–1337zbMATHGoogle Scholar
  32. 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):7–15Google Scholar
  33. 33.
    Li X, Baki M, Aneja YP (2010) An ant colony optimization metaheuristic for machine–part cell formation problems. Comput Oper Res 37(12):2071–2081zbMATHGoogle Scholar
  34. 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):1566–1576Google Scholar
  35. 35.
    Jabbarzadeh A, Jalali Naini SG, Davoudpour H, Azad N (2012) Designing a supply chain network under the risk of disruptions. Math Probl Eng 2012Google Scholar
  36. 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 70:225–244Google Scholar
  37. 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:1–30Google Scholar
  38. 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 183:700–709Google Scholar
  39. 39.
    Jabbarzadeh A, Fahimnia B, Sheu J-B (2017) An enhanced robustness approach for managing supply and demand uncertainties. Int J Prod Econ 183:620–631Google Scholar
  40. 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 94:121–149Google Scholar
  41. 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 91:306–324Google Scholar
  42. 42.
    Shishebori D, Jabalameli MS, Jabbarzadeh A (2013). Facility location-network design problem: reliability and investment budget constraint. J Urban Plann Dev 140, 04014005Google Scholar
  43. 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 43:139–149Google Scholar
  44. 44.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHGoogle Scholar
  45. 45.
    Liu B (1999) Uncertain programming a Wiley-Interscience publication, New YorkGoogle Scholar
  46. 46.
    Zimmermann H-J (2001) Fuzzy set theory—and its applications. Springer Science and Business Media, BerlinGoogle Scholar
  47. 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):215–236MathSciNetzbMATHGoogle Scholar
  48. 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 213–227zbMATHGoogle Scholar
  49. 49.
    Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159(2):193–214MathSciNetzbMATHGoogle Scholar
  50. 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):251–265Google Scholar
  51. 51.
    Arikan F, Güngör Z (2005) A parametric model for cell formation and exceptional elements’ problems with fuzzy parameters. J Intell Manuf 16(1):103–114Google Scholar
  52. 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):164–168Google Scholar
  53. 53.
    Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci 36(1):59–83MathSciNetzbMATHGoogle Scholar
  54. 54.
    Ross TJ (2016) Fuzzy logic with engineering applications. John Wiley & Sons, New MexicoGoogle Scholar
  55. 55.
    Kennedy J, Eberhart R Particle swarm optimization. In: Proceedings of IEEE international conference on. neural networks, 1995. Perth, Australia, pp 1942–1948Google Scholar
  56. 56.
    Kirkpatrick S Jr, DG VMP (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetzbMATHGoogle Scholar
  57. 57.
    Shafer S, Rogers D (1993) Similarity and distance measures for cellular manufacturing. Part I. A survey. Int J Prod Res 31(5):1133–1142Google Scholar
  58. 58.
    Kohonen T (2001) Self-organizing maps, vol 30. Springer, BerlinzbMATHGoogle Scholar
  59. 59.
    P Chandrasekharan M, Rajagopalan R (1986) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24(2):451–463zbMATHGoogle Scholar
  60. 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 Google Scholar
  61. 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 Google Scholar
  62. 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 zbMATHGoogle Scholar
  63. 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 Google Scholar
  64. 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 Google Scholar
  65. 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 Google Scholar
  66. 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 zbMATHGoogle Scholar
  67. 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 Google Scholar
  68. 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 Google Scholar
  69. 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 Google Scholar
  70. 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 Google Scholar
  71. 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 Google Scholar
  72. 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 Google Scholar
  73. 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 Google Scholar
  74. 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 Google Scholar
  75. 75.
    Ravi Kumar K, Vannelli A (1987) Strategic subcontracting for efficient disaggregated manufacturing. Int J Prod Res 25(12):1715–1728Google Scholar
  76. 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 Google Scholar
  77. 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 zbMATHGoogle Scholar
  78. 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 Google Scholar
  79. 79.
    Wemmerlov U, Johnson DJ (1997) Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int J Prod Res 35(1):29–49zbMATHGoogle Scholar
  80. 80.
    Singh N, Rajamani D (2012) Cellular manufacturing systems: design, planning and control. Springer Science and Business Media, BerlinGoogle Scholar
  81. 81.
    Haykin SS (2009) Neural networks and learning machines, vol 3. Pearson Education, Upper Saddle RiverGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Vahid Mahmoodian
    • 1
  • Armin Jabbarzadeh
    • 1
    Email author
  • Hassan Rezazadeh
    • 2
  • Farnaz Barzinpour
    • 1
  1. 1.Department of Industrial EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Department of Industrial EngineeringUniversity of TabrizTabrizIran

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