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Partitioning of a manufacturing system into machine cells—a practical application

Abstract

This article focuses on partitioning a manufacturing system into machine cells. The benefit of such a conversion is improving manufacturing efficiency by eliminating waste in terms of material handling and time as well as increasing production capacity of the manufacturing system. Rank order clustering (ROC) algorithm is used in this study for the conversion. It is easy to understand and its application in obtaining machine cells with a cellular layout is straightforward. ROC algorithm requires a part-machine incidence matrix. The matrix is rearranged until multiple part families and corresponding machine cells are obtained. MS Excel and MATLAB are used in the application. The study converts 44 machine manufacturing system into seven machine cells and brings important performance improvement.

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References

  • Ahkioon S, Bulgak AA, Bektas T (2009) Cellular manufacturing systems design with routing flexibility, machine procurement, production planning and dynamic system reconfiguration. Int J Prod Res 47(6):1573–1600

    MATH  Google Scholar 

  • Alcorta L (2003) Flexible Automation in Developing Countries: The Impact on Scale and Scope and the Implications for Location of Production. UNU/INTECH Studies in New Technology and Development, Routledge

  • Alessandra MS, Bruno JT, Fernandes CJA, Bastos F (2018) Pyramidal neural networks with evolved variable receptive fields. Neural Comput Appl 29(12):1443–1453

    Google Scholar 

  • Amruthnath N, Gupta T (2016) Modified rank order clustering algorithm approach by including manufacturing data. IFAC Pap Line 49(5):138–142

    Google Scholar 

  • Banerjee I, Das P (2012) Group technology based adaptive cell formation using predator–prey genetic algorithm. Appl Soft Comput 12:559–572

    Google Scholar 

  • Bazargan-Lari M (1999) Layout designs in cellular manufacturing. Eur J Oper Res 112:258–272

    MATH  Google Scholar 

  • Borenstein D (1998a) A visual interactive multi criteria decision analysis model for FMS design. Int J Adv Manuf Technol 14:848–857

    Google Scholar 

  • Borenstein D (1998b) Intelligent decision support system for flexible manufacturing system design. Ann Oper Res 77:129–156

    MATH  Google Scholar 

  • Bramhane R, Arora A, Chandra H (2014) Simulation of flexible manufacturing system using adaptive neuro fuzzy hybrid structure for efficient job sequencing and routing. Int J Mech Eng Robot Res 3(4):33–48

    Google Scholar 

  • Chan FTS, Jiang B, Tang NKH (2000) The development of intelligent decision support tools to aid the design of flexible manufacturing systems. Int J Prod Econ 65:73–84

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  • Chang C-C, Wu T-H, Wu C-W (2013) An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems. Comput Ind Eng 66:438–450

    Google Scholar 

  • Dimopoulos C, Mort N (2001) A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. Int J Prod Res 39(1):1–19

    MATH  Google Scholar 

  • Goncalves JF, Resende MGC (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47:247–273

    Google Scholar 

  • Groover MP (2008) Automation production systems and computer—integrated manufacturing, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Gupta T, Seifoddini HI (1990) Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. Int J Prod Res 28(7):1247–1269

    Google Scholar 

  • Hameri A-P (2011) Production flow analysis—cases from manufacturing and service industry. Int J Prod Econ 129:233–241

    Google Scholar 

  • He Y, Stecke KE, Smith ML (2016) Robot and machine scheduling with state-dependent part input sequencing in flexible manufacturing systems. Int J Prod Res 54(22):6736–6746

    Google Scholar 

  • Kaufmann L (2005) China champions: how german companies can successfully integrate china into their global strategies. WHU book series on international expansion strategies, European Management Publications

  • Khan MK, Gwee SH (1997) Plant layout improvements to a medium volume manufacturing system using systematic techniques to form just-in-time manufacturing cells. Proc Instn Mech Engrs Part B 211:109–124

    Google Scholar 

  • Khannan MSA, Maruf A (2012) Development of robust and redesigning cellular manufacturing system model considering routing flexibility, setup cost, and demand changes. In: Kachitvichyanukul HT Luong, Pitakaso R (eds) Proceedings of the Asia Pacific Industrial Engineering and Management Systems Conference 2012V, 1969–1977

  • Kia R, Baboli A, Javadian N, Tavakkoli-Moghaddam R, Kazemi M, Khorrami J (2012) Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing. Comput Oper Res 39:2642–2658

    MathSciNet  MATH  Google Scholar 

  • King JR (1980) Machine–component grouping in production flow analysis: an approach using a rank order clustering algorithm. Int J Prod Res 18(2):213–232

    Google Scholar 

  • Kusiak A (1987) The generalized group technology concept. Int J Prod Res 25(4):561–569

    Google Scholar 

  • Kusiak A (1988) EXGT-S: a knowledge based system for group technology. Int J Prod Res 26(5):887–904

    Google Scholar 

  • Lee MK, Luong HS, Abhary K (1997) Genetic algorithm based cell design considering alternative routing. Comput Integr Manuf Syst 10(2):93–108

    Google Scholar 

  • Lei D, Wu Z (2005) Tabu search-based approach to multi-objective machine part cell formation. Int J Prod Res 43:5241–5252

    MATH  Google Scholar 

  • Li X, Li H, Sun B, Wang F (2018) Assessing information security risk for an evolving smart city based on fuzzy and grey FMEA. J Intell Fuzzy Syst 34(4):2491–2501

    Google Scholar 

  • Liu Y, Wang Z, Yuan Y, Alsaadi FE (2018) Partial-nodes-based state estimation for complex networks with unbounded distributed delays. IEEE Trans Neural Netw Learn Syst 29(8):3906–3912

    MathSciNet  Google Scholar 

  • Mahdavi I, Teymourian E, Baher NT, Kayvanfar V (2013) An integrated model for solving cell formation and cell layout problem simultaneously considering new situations. J Manuf Syst 32:655–663

    Google Scholar 

  • Reddy BSP, Rao CSP (2006) A hybrid multi objective GA for simultaneous scheduling of machines and AGV in FMS. Int J Adv Manuf Technol 31:602–613

    Google Scholar 

  • Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309

    Google Scholar 

  • Rubio JJ (2018) Error convergence analysis of the SUFIN and CSUFIN. Appl Soft Comput 72:587–595

    Google Scholar 

  • Rubio JJ, Lughofer E, Meda-Campaña J, Páramo LA, Novoa JF, Pacheco J (2018) Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models. J Intell Fuzzy Syst 35(2):2585–2596

    Google Scholar 

  • Shin KS, Park JO, Kim YK (2011) Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm. Comput Oper Res 38:702–712

    MathSciNet  MATH  Google Scholar 

  • Stecke KE (1983) Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems. Manag Sci 29(3):273–288

    MATH  Google Scholar 

  • Stecke KE (1986) A hierarchical approach to solve machine grouping and loading problem of FMS. Eur J Oper Res 24:369–378

    MATH  Google Scholar 

  • Su C, Hsu CM (1998) Multi-objective machine-part cell formation through parallel simulated annealing. Int J Prod Res 36(8):2185–2207

    MATH  Google Scholar 

  • Suresh NC (1990) Toward an integrated evaluation of flexible automation investment. Int J Prod Res 28:1657–1672

    Google Scholar 

  • Uddin MK, Shanker K (2002) Grouping of parts and machines in presence of alternative process routes by genetic algorithm. Int J Prod Econ 76(3):219–228

    Google Scholar 

  • Wu TH, Chang C, Chung S (2000) A simulated annealing algorithm to manufacturing cell formation problems. Expert Syst Appl 34:1609–1617

    Google Scholar 

  • Yadav A, Jayswal SC (2018) Modelling of flexible manufacturing system: a review. Int J Prod Res 56(7):2464–2487

    Google Scholar 

  • Zhao C, Wu Z (2000) A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. Int J Prod Res 38(2):385–395

    MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Company X name of which has been kept confidential upon request for providing facilities to carry out the experiments at their work site.

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All authors read and approved the final manuscript.

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Correspondence to Yusuf Tansel İç.

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Appendices

Appendix A: A partial showing of parts with their codes and numbers

Part Code

Part No

Part Code

Part No

Part Code

Part No

Part Code

Part No

1

3P5737

31

1S5331

61

6J9324

91

5V2975

2

4E4524

32

3P2856

62

7P7740

92

2S6150

3

130-1918

33

2M0125

63

101-3374

93

9J7084

4

9G1491

34

4N2697

64

8P0953

94

3P0330

5

8D7427

35

6Y3545

65

5M7306

95

9T2464

6

2P1129

36

1P1370

66

4S5862

96

8J1816

7

9T2466

37

155-6428

67

4V6430

97

9T8994

8

7I7180

38

6Y3545

68

7G0857

98

113-1150

9

7C8367

39

1T0436

69

4V7105

99

YS58400911

10

8E4026

40

6G1953

70

5V1882

100

7J9873

11

115-6425

41

5V1508

71

115-6428

101

3T5928

12

195-13-11143

42

8V2539

72

3P1626

102

9Y5618

13

3G5502

43

4V7108

73

7T9647

103

5Y1511

14

1V9148

44

9T6906

74

9S3955

104

6P0169

15

1V9148

45

2P4523

75

9P9575

105

4Y1034

16

4Y7089

46

6E4985

76

7J9357

106

7V6715

17

115-3183

47

8K1946

77

7J9683

107

3G6058

18

192-6448

48

4V7082

78

125-7894

108

7J9845

19

YK1163011801

49

132-2463

79

3P0785

109

7Y0663

20

6E4869

50

3S7517

80

5J5365

110

3G5281

21

1162010801

51

2G8630

81

8E6752

111

175-71-21261

22

8N9622

52

1T1624

82

4N9325

112

3G7648

23

8N8980

53

9G7544

83

4S5980

113

3G8427

24

YK1172011000

54

110-7058

84

2W4087

114

1T1908

25

8N8980

55

9C6032

85

1W4407

115

7J9778

26

9C9191

56

9V3853

86

275-0120

116

9P0202

27

9D1177

57

7T4865

87

8V6384

117

9W0635

28

7W3773

58

8W5293

88

5M7341

118

9K8425

29

YK1172504600

59

8W6497

89

8H3325

119

8V7659

30

YK1162504501

60

116-8538

90

7K6432

120

6E1103

241

193-1152

260

3T5928

279

3W8392

298

5V6328

242

195-9703

261

175-63-13630

280

6K0053

299

9V3668

243

1T1569

262

3T1378

281

6G1539

300

7T3370

244

YK1171101001

263

8V1624

282

5K0991

301

1P7521

Appendix B

See Fig. 7.

Fig. 7
figure 7

Type, models and codes of the CNC machine tools

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İç, Y.T., Ağca, B.V. & Yurdakul, M. Partitioning of a manufacturing system into machine cells—a practical application. Evolving Systems 12, 423–438 (2021). https://doi.org/10.1007/s12530-019-09301-9

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  • DOI: https://doi.org/10.1007/s12530-019-09301-9

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