Abstract
This research develops a new methodology for a cell formation problem in the cellular manufacturing system. The methodology includes two phases. In the first phase, an improved similarity coefficient method, which considers the operation sequence and the number of repeated operations firstly in related researches, is proposed to identify part families. A new decomposed mathematical model is presented in the second phase, which considers some crucial operational aspects such as alternative routing, machine capacity, part demand, operation time, and lot splitting, to assign machines into part families for minimum machine cost, operation cost, and inter-cell movement cost. The model puts emphasis on the effect of trade-off between machine duplication and material inter-cell movement on performance of the cell formation to optimize machine utilization and workload balance. This paper also provides a concrete production schedule with optimum system utilization for cell formation. Test problems and sensitivity analyses are carried out to reveal the effectiveness and feasibility of the proposed methodology.
Similar content being viewed by others
References
Ahkioon S, Bulgak AA, Bektas T (2009) Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. Eur J Oper Res 192(2):414–428
Anderberg MR (1973) Cluster analysis for applications. Academic, New York, pp 84–85
Bajestani MA, Rabbani M, Rahimi-Vahed AR, Khoshkhou GB (2009) A multi-objective scatter search for a dynamic cell formation problem. Comput Oper Res 36(3):777–794
Ballakur A, Steudel H (1987) A within-cell utilization based heuristic for designing cellular manufacturing systems. Int J Prod Res 25(5):639–665
Baroni-Urbani C, Buser MW (1976) Similarity of binary data. Syst Biol 25(3):251–259
Chen MY (1998) A mathematical programming model for system reconfiguration in a dynamic cellular manufacturing environment. Ann Oper Res 77:109–128
Chen HW, Wang AM, Ning RX (2013) Formation of mixed configuration cell based on product demand prediction. Int J Adv Manuf Technol 66:417–430
Dalfard VM (2013) New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements. Appl Math Model 37(4):1884–1896
Defersha FM, Chen MY (2008) A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. Eur J Oper Res 187(1):46–69
Diaby M, Nsakanda AL (2006) Large scale capacitated part-routing in the presence of process and routing flexibilities and setup costs. J Oper Res Soc 57:1100–1112
Foulds LR, French AP, Wilson JM (2006) The sustainable cell formation problem: manufacturing cell creation with machine modification costs. Comput Oper Res 33(4):1010–1032
Green TJ, Sadowski RP (1984) A review of cellular manufacturing assumptions, advantages and design techniques. J Oper Manag 4(2):85–97
Gunasingh KR, Lashkari RS (1989) The cell formation problem in cellular manufacturing systems—a sequential modelling approach. Comput Ind Eng 16(4):469–476
Gupta T (1993) Design of manufacturing cells for flexible environment considering alternative routing. Int J Prod Res 31(6):1259–1273
Gupta T, Seifoddini H (1990) Production data based similarity coefficient or machine-component grouping decisions in the design of a cellular manufacturing system. Int J Prod Res 28(7):1247–1269
Hachicha W, Masmoudi F, Haddar M (2008) Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach. Int J Adv Manuf Technol 36:1157–1169
Holley JW, Guilford JP (1964) A note on the G index of agreement. Educ Psychol Meas 24:749–753
Islam KMS, Sarker BR (2000) A similarity coefficient measure and machine–parts grouping in cellular manufacturing systems. Int J Prod Res 38(3):699–720
Jeon G, Leep H (2006) Forming part families by using genetic algorithm and designing machine cells under demand changes. Comput Oper Res 33(1):263–283
Kamrani AK, Parsoei HR, Chandhry MA (1993) A survey of design methods for manufacturing cells. Comput Ind Eng 25(1–4):487–490
Kao Y, Chen CC (2013) A differential evolution fuzzy clustering approach to machine cell formation. Int J Adv Manuf Technol 65:1247–1259
Krishnan KK, Mirzaei S, Venkatasamy V, Pillai VM (2012) A comprehensive approach to facility layout design and cell formation. Int J Adv Manuf Technol 59:737–753
Kusiak A, Cho M (1992) Similarity coefficient algorithms for solving the group technology problem. Int J Prod Res 30(11):2633–2646
Mansouri SA, Moattar-Husseini SH, Zegordi SH (2003) A genetic algorithm for multiple objective dealing with exceptional elements in cellular manufacturing. Prod Plan Control 14(5):437–446
McAuley J (1972) Machine grouping for efficiency production. Prod Eng 51(2):53–57
Offodile OF (1991) Application of similarity coefficient method to parts coding and classification analysis in group technology. J Manuf Syst 10(6):442–448
Offodile OF, Mehrez A, Grznar J (1994) Cellular manufacturing: a taxonomic review framework. J Manuf Syst 13(3):196–220
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
Romesburg HC (1984) Cluster analysis for researchers. Lifetime Learning Publications, Belmont
Safaei N, Saidi-Mehrabad M, Jabal-Ameli MS (2008) A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. Eur J Oper Res 185(2):563–592
Sarker BR, Xu Y (1998) Operation sequences-based cell formation methods: a critical survey. Prod Plan Control 9(8):771–783
Selim HM, Askin RG, Vakharia AJ (1998) Cell formation in group technology: review, evaluation and directions for future research. Comput Ind Eng 34(1):3–20
Szwarc D, Rajamani D, Bectort CR (1997) Cell formation considering fuzzy demand and machine capacity. Int J Adv Manuf Technol 13:134–147
Tavakkoli-Moghaddam R, Minaeian S, Rabbani S (2008) A new multi-objective model for dynamic cell formation problem with fuzzy parameters. Int J Eng Trans A 21(2):159–172
Vakharia AJ, Wemmerlov U (1990) Designing a cellular manufacturing system: a materials flow approach based on operation sequences. IIE Trans 22(1):84–97
Waghodekar PH, Sahu S (1984) Machine-component cell formation in group technology. Int J Prod Res 28(6):937–948
Wicks EM, Reasor RJ (1991) Designing cellular manufacturing systems with dynamic part populations. IIE Trans 31(1):11–20
Wilhelm WE, Chiou CC, Chang DB (1998) Integrating design and planning considerations in cellular manufacturing. Ann Oper Res 77:97–107
Witte JD (1980) The use of similarity coefficients in production flow analysis. Int J Prod Res 18(4):503–514
Won Y, Lee KC (2001) Group technology cell formation considering operation sequences and production volumes. Int J Prod Res 39(13):2755–2768
Yasuda K, Hu L, Yin Y (2005) A grouping genetic algorithm for the multi-objective cell formation problem. Int J Prod Res 43(4):829–853
Yin Y, Yasuda K (2002) Modification of existing similarity coefficients by considering an operation sequence ratio in designing cellular manufacturing systems. Ind Eng Manag Syst 1(1):19–28
Yin Y, Yasuda K (2006) Similarity coefficient methods applied to the cell formation problem: a taxonomy and review. Int J Prod Econ 101(2):329–352
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wu, L., Suzuki, S. Cell formation design with improved similarity coefficient method and decomposed mathematical model. Int J Adv Manuf Technol 79, 1335–1352 (2015). https://doi.org/10.1007/s00170-015-6931-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-6931-7