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
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
Amruthnath N, Gupta T (2016) Modified rank order clustering algorithm approach by including manufacturing data. IFAC Pap Line 49(5):138–142
Banerjee I, Das P (2012) Group technology based adaptive cell formation using predator–prey genetic algorithm. Appl Soft Comput 12:559–572
Bazargan-Lari M (1999) Layout designs in cellular manufacturing. Eur J Oper Res 112:258–272
Borenstein D (1998a) A visual interactive multi criteria decision analysis model for FMS design. Int J Adv Manuf Technol 14:848–857
Borenstein D (1998b) Intelligent decision support system for flexible manufacturing system design. Ann Oper Res 77:129–156
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
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
Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1(1):65–75
Chandrasekharan MP, Rajagopalan R (1986) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24(2):451–463
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
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
Goncalves JF, Resende MGC (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47:247–273
Groover MP (2008) Automation production systems and computer—integrated manufacturing, 3rd edn. Prentice Hall, Upper Saddle River
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
Hameri A-P (2011) Production flow analysis—cases from manufacturing and service industry. Int J Prod Econ 129:233–241
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
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
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
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
Kusiak A (1987) The generalized group technology concept. Int J Prod Res 25(4):561–569
Kusiak A (1988) EXGT-S: a knowledge based system for group technology. Int J Prod Res 26(5):887–904
Lee MK, Luong HS, Abhary K (1997) Genetic algorithm based cell design considering alternative routing. Comput Integr Manuf Syst 10(2):93–108
Lei D, Wu Z (2005) Tabu search-based approach to multi-objective machine part cell formation. Int J Prod Res 43:5241–5252
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
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
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
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
Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309
Rubio JJ (2018) Error convergence analysis of the SUFIN and CSUFIN. Appl Soft Comput 72:587–595
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
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
Stecke KE (1983) Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems. Manag Sci 29(3):273–288
Stecke KE (1986) A hierarchical approach to solve machine grouping and loading problem of FMS. Eur J Oper Res 24:369–378
Su C, Hsu CM (1998) Multi-objective machine-part cell formation through parallel simulated annealing. Int J Prod Res 36(8):2185–2207
Suresh NC (1990) Toward an integrated evaluation of flexible automation investment. Int J Prod Res 28:1657–1672
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
Wu TH, Chang C, Chung S (2000) A simulated annealing algorithm to manufacturing cell formation problems. Expert Syst Appl 34:1609–1617
Yadav A, Jayswal SC (2018) Modelling of flexible manufacturing system: a review. Int J Prod Res 56(7):2464–2487
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
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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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.
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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