Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method
Abstract
Over the last four decades of research, numerous cell formation algorithms have been developed and tested, still this research remains of interest to this day. Appropriate manufacturing cells formation is the first step in designing a cellular manufacturing system. In cellular manufacturing, consideration to manufacturing flexibility and productionrelated data is vital for cell formation. The consideration to this realistic data makes cell formation problem very complex and tedious. It leads to the invention and implementation of highly advanced and complex cell formation methods. In this paper an effort has been made to develop a simple and easy to understand/implement manufacturing cell formation heuristic procedure with considerations to the number of production and manufacturing flexibilityrelated parameters. The heuristic minimizes intercellular movement cost/time. Further, the proposed heuristic is modified for the application of principal component analysis and Taguchi’s method. Numerical example is explained to illustrate the approach. A refinement in the results is observed with adoption of principal component analysis and Taguchi’s method.
Keywords
Cellular manufacturing Cell formation Manufacturing flexibility Production data Principal component analysis Taguchi’s methodIntroduction
Summary of work observed on cell formation using production or manufacturing flexibilityrelated data
Author and year  Parameter considered  Approach/remarks 

Kumar and Sharma (2014)  Operation sequence, production volume, intercell movement cost, part processing cost, alternate process plans  Proposed similarity coefficientbased heuristic 
Lian et al. (2013)  Multiple identical machines, processing time, setup time, machine capacity, production volume, cell size, alternative routes  Proposed genetic algorithm (GA)based procedure 
Gupta et al. (2012)  Operation sequence  Similarity coefficient, principal component analysis (PCA), Kmeans algorithm 
Kumar and Jain (2010)  Operation sequence, operation time, production volume, machine capacity  Proposed a PCAbased concurrent algorithm “APMOSTVC” 
Ahi et al. (2009)  Operational time, operation sequence  TOPSIS and SAW 
Pandian and Mahapatra (2009)  Operation sequence, operation time  Adaptive resonance theory, neural network 
Paydar and Sahebjamnia (2009)  Operation sequence  Proposed a linear mathematical programming model 
Susanto et al. (2009)  Sequence of operations, partvolume, alternative routes  Cmeans clustering algorithm, Hungarian (assignment) algorithm, linear programming model 
Garbie et al. (2008)  Alternative routings, processing time, machine capacity (reliability), machine capability (flexibility), production volume, part demand, number of operations done on each machine  Proposed similarity coefficientbased heuristic 
Muruganandam et al. (2008)  Demand of parts in different period, routing sequences, processing time, machine capacities  Proposed a GA based heuristic “PRABHA” 
Kumar and Jain (2008)  Operation sequence, operation time, production volume, intercellular travel loss  Proposed an algorithm “APOSTVUIT” based on average void values and PCA 
Masmoudi et al. (2008)  Alternative routes  Combined axiomatic design principles with experimental design technique, and PCA 
Kim et al. (2004)  Machine sequence of part routes, alternative routes, machine work load imbalance  Proposed a two phased heuristic algorithmbased on dissimilarity measure 
Mahesh and Srinivasan (2002)  Processing time, alternative routes  Branch and bound technique, a heuristic based multistage programming approach 
Mukattash et al. (2002)  Multiple parallel machines, processing time, alternative routes  Proposed three heuristics 
Won and Lee (2001)  Operation sequence and production volume, with extension for intercell material handling cost and processing times  Mathematical model that seeks to minimize the actual intercell flows 
Sofianopoulou (1999)  Operation sequence  Proposed simulated annealingbased algorithm 
Wicks and Reasor (1999)  Operation sequence, production volume  Proposed genetic algorithmbased procedure 
Nair and Narendran 1998  Operation sequence  Proposed measures of similarity and performance are incorporated in a nonhierarchical clustering algorithm: “CASE” 
Beaulieu et al. (1997)  Machines and material handling costs, machine utilization, alternative routeings, Intercell movement  Presented algorithm has two main resolution phases: formation of independent cells then introduction of intercell movements 
Beaulieu et al. (1993)  Work load balance, machine flexibility, routing flexibility  Developed heuristic 
Ahmed et al. (1991)  Production volume, material handling cost  Proposed heuristic for minimization of total material handling cost 
The outline of rest of the paper is as follows: "Methodology" explains the methodology and proposed heuristic for solving the CF problem. "Implementation and illustration" illustrates implementation of proposed heuristic through a numerical problem. In discussion and analysis part i.e. "Discussion and analysis", results of clustering algorithm used, are compared with some wellknown CF algorithms. Further in this section, proposed algorithm is modified for the implementation of PCA and Taguchi’s method, whilst conclusions are drawn in "Conclusions".
Methodology
The proposed heuristic is a development in the work of Kumar and Sharma (2014). The simple logic used for consideration to operation sequence is that a machine could add maximum one intercell move per part if it is either at starting or at ending position of the operation sequence of a particular part, otherwise it could add maximum two intercell moves (Won and Lee 2001). Taking inspiration of Leem and Chen (1996) the concept of partoperation incidence (POI) matrix is used in proposed CF procedure. Albadawi et al. (2005), Hachicha et al. (2006, 2008a) highlighted the application of PCA in solving CF problems. Hachicha et al. (2008b) used Taguchi’s method along with PCA in route selection of CF problems. Applications of PCA and Taguchi’s method are introduced in modification of proposed heuristic.
The methodology adopted is discussed under four subtitles namely proposed CF heuristic procedure, commonality score/similarity coefficientbased clustering algorithm, PCA and Taguchi’s method.
Proposed CF heuristic procedure
Commonality scorebased clustering algorithm
Though any similarity scorebased clustering algorithm can be used with the proposed procedure, the commonality scorebased clustering algorithm used is discussed in illustration in this section. The commonality score is used to deduce similarity matrix amongst all possible machine pairs.
Taguchi’s method

System design: to find the suitable working levels of the design factors.

Parameter design: to determine the factor levels for the optimum performance of the product or process.

Tolerance design: to refine the results of parameter design by narrowing the tolerance levels of factors that have significant effects on the product or process under study.
 1.Planning experiment

Determination of the control factors, noise factors and quality or performance measure responses of the product or process.

Determination of the levels of each factor.

Selection of a most suitable OA table. It depends on the number of factors and interactions, and the number of levels for the factors.

 2.
Implementing experiment.
 3.Analysing and examining result.

Determination of the parameters signification (ANOVA).

Conduct a main effect plot analysis to determine the optimal level of the control factors.

Execute a factor contribution rate analysis.

Confirm experiment and plan future application.

The intended use of Taguchi’s method is in the selection of best part routings for each part type. The essence of proposed CF procedure is to minimize the intercell movement time/cost, which cannot be used as a response measure for Taguchi’s method due to large dimensions of variables. To overcome this situation, PCA, a dimensionreduction technique is employed.
Principal component analysis
Principal component analysis is the most widely used dimensionreduction statistical technique. It investigates the largely widespread data in many areas of science and industry. It provides a condensed description (Hachicha et al. 2008a; Kumar and Jain 2010), in order to model the total variance of the original data set, through new uncorrelated variables known as principal components. These components recover as much variability in the data as possible and account for near total variance of the data. Principal component analysis is recommended for large sample sizes (Gupta et al. 2012; Hachicha et al. 2008a; Mehrjoo and Bashiri 2013).The usual progression of PCA starts with the eigenvalues and eigenvector of semidefinite matrix. A brief description on implementation of PCA is as follows:
Let, the initial matrix (A) be a semidefinite matrix, in which rows and columns stand for part (P) and machines (M) respectively, having the information like partmachine incidence, operation sequence, production volume and intercell movement time/cost. Since CF problem is a dimensionreduction problem in which a number of interrelated machines and parts are to be grouped into a smaller set of independent cells, the application of principal components analysis can give a very good solution as mentioned by (Albadawi et al. 2005; Gupta et al. 2012; Hachicha et al. 2006, 2008b) quickly.
B is the standardization matrix of the initial matrix A, B ^{T} is the transpose matrix of matrix B
For more details of PCA method, and its application in cell formation relevant literature such as (Albadawi et al. 2005; Chattopadhyay et al. 2012; Gupta et al. 2012; Hachicha et al. 2006, 2008a, b; Llin et al. 2010; Kumar and Jain 2010; Mehrjoo and Bashiri 2013; Min et al. 2014) and others can be referred.
In this work, PCA finds its application in two ways, first in reduction of variability for implementation of Taguchi’s method in selection of alternate route, second in the making of operation and part families through graphical analysis.
Implementation and illustration
The proposed heuristic CF procedure is implemented on an arbitrarily designed CF problem illustrated in "Illustrative problem".
Illustrative problem
Initial data for cell formation illustrative problem
Operation  Machine  Reliability  Operation time  Part  Alternate process plans (routings)  Interchangeability of operation  Production volume  Intercell movement time 

O1  M1  0.9  10  P1  O1 → O3 → O2 → O5  O1 ↔ O3  100  1 
M2  0.8  10  O2 → O4 → O1  O4 ↔ O2  
O2  M3  0.9  15  P2  O1 → O2 → O5  Nil  50  4 
M4  1.0  15  O3 → O1 → O2 → O4  O3 ↔ O1  
O3  M5  0.7  25  P3  O4 → O2 → O1 → O5  O2 ↔ O1  70  3 
O4  M6  0.9  20  P4  O4 → O3 → O2 → O3 → O1  O4 ↔ O2  65  2 
O5  M7  1.0  15  P5  O1 → O5 → O4  Nil  75  2 
M8  0.85  15  O3 → O4 → O5 → O4  Nil 
POI matrix for illustration
Part  P1  P2  P3  P4  P5  

Process plan  1  2  3  4  5  6  7  8 
O1  1  1  1  1  1  1  1  0 
O2  1  1  1  1  1  1  0  0 
O3  1  0  0  1  0  1  0  1 
O4  0  1  0  1  1  1  1  1 
O5  1  0  1  0  1  0  1  1 
POI matrix after consideration to production volume
Part  P1  P2  P3  P4  P5  

Process plan  1  2  3  4  5  6  7  8 
O1  100  100  50  50  70  65  75  0 
O2  100  100  50  50  70  65  0  0 
O3  100  0  0  50  0  65  0  75 
O4  0  100  0  50  70  65  75  75 
O5  100  0  50  0  70  0  75  75 
Maximum possible intercell moves matrix
Part  P1  P2  P3  P4  P5  

Process Plan  1  2  3  4  5  6  7  8 
O1  100  100  50  100  140  65  75  0 
O2  200  100  100  100  140  130  0  0 
O3  200  0  0  50  0  260  0  75 
O4  0  200  0  50  70  65  75  210 
O5  100  0  50  0  70  0  150  150 
Maximum possible intercell movement time matrix
Part  P1  P2  P3  P4  P5  

Process plan  1  2  3  4  5  6  7  8 
O1  100  100  200  400  420  130  150  0 
O2  200  100  400  400  420  260  0  0 
O3  200  0  0  200  0  520  0  150 
O4  0  200  0  200  210  130  150  420 
O5  100  0  200  0  210  0  300  300 
Matrix after clustering of operation cells
Part  P1  P2  P3  P4  P5  

Process plan  1  2  3  4  5  6  7  8 
Operation cell 1  
O1  100  100  200  400  420  130  150  0 
O2  200  100  400  400  420  260  0  0 
Operation cell 2  
O3  200  0  0  200  0  520  0  150 
O4  0  200  0  200  210  130  150  420 
O5  100  0  200  0  210  0  300  300 
Max. Intercellular movement time (min value)  300  200  200  400  420  390  150  0 
Part in operation cell  Any  Any  1  1  1  2  2  2 
Matrix after clustering of operation cells and operation flexibility
Part  P1  P2  P3  P4  P5  

Process plan  1  2  3  4  5  6  7  8 
Operation cell 1  
O1  200  100  200  200  420  130  0  0 
O2  200  200  400  400  420  130  150  0 
Operation cell 2  
O3  100  0  0  400  0  520  0  150 
O4  0  100  0  200  210  260  150  420 
O5  100  0  200  0  210  0  300  300 
Max. intercellular movement time (min. value)  200  100  200  600  420  260  150  0 
Part in operation cell  1  1  1  any  1  2  2  2 
Operation cell after selection of process plan and part assignment
At this stage, total intercell moves and intercell movement cost for required production volume are 1,210 and 585 units, respectively.
Final clustered matrix containing manufacturing cell with assigned machines and parts
Thus, total intercell moves and intercell movement cost for required production volume are 300 and 570 units, respectively.
Discussion and analysis
The discussion and analysis is performed in two subsections. In first subsection results from clustering algorithm used, are compared with the results of some wellestablished binary matrixbased CF methods. In second subsection, the modified proposed CF procedure for adoption PCA and Taguchi’s method is presented.
Comparison of results of clustering algorithm used
Performance comparison of clus tering algorithm used against some established binary matrixbased CF methods
Source of problem  Size of problem (part × machine)  Performance measure  

Procedure used  Source author’s method  
EE  GE  GEF  GI  GM  EE  GE  GEF  GI  GM  
Elbenani and Ferland (2012)  8 × 6  6  88.89  67.44  74.54  68.35  6  88.89  67.44  74.54  68.35 
Gupta et al. (2012)  11 × 7  1  80.3  62.5  70.3  54.29  1  80.3  62.5  70.3  54.29 
Ghosh and Dan (2011)  7 × 5  3  54.3  69.6  85  75.6  3  54.3  69.6  85  75.6 
Doulabi S H et al. (2009)  8 × 6  2  87.06  76.92  77.78  74.24  2  87.06  76.92  77.78  74.24 
Hachicha et al. (2006)  11 × 7  2  86.1  70.37  72.41  66.47  2  86.1  70.37  72.41  66.47 
Albadawi et al. (2005)  20 × 8  9  95.8  85.2  85.2  1  9  95.8  85.2  85.2  1 
Modified proposed heuristic
Route selection through PCA, and Taguchi’s method
The L_{8} orthogonal array and CP measure
S. no.  P1  P2  P5  CP 

1  1  3  7  91.36 
2  1  3  8  92.4 
3  1  4  7  92.58 
4  1  4  8  97.13 
5  2  3  7  71.34 
6  2  3  8  88.63 
7  2  4  7  91.36 
8  2  4  8  99.58 
Maximum possible intercell movement time matrix after route selection
Part  P1  P2  P3  P4  P5 

Process plan  1  4  5  6  8 
O1  100  400  420  130  0 
O2  200  400  420  260  0 
O3  200  200  0  520  150 
O4  0  200  210  130  420 
O5  100  0  210  0  300 
Clustered Matrix before machine assignment and operation flexibility
Final clustered matrix after route selection through PCA and Taguchi’s method
Thus, total intercell moves and intercell movement cost for required production volume are 285 and 640 units, respectively.
In comparing the two solutions of same illustrative problem presented in Tables 10 and 15 (route selection through PCA and Taguchi’s method), it is observed that the implementation of PCA and Taguchi’s method only in route selection decreases the total intercell moves whilst a slight increase in total intercell movement cost is also there.
Clustering of operations with PCAbased graphical analysis

Two neighbouring operations with a small angle distance measure → Operations belong to the same cell. (‘O4’ and ‘O5’, ‘O1’ and ‘O2’ in Fig. 5).

Two operations with angle distance measurement between them is almost 180°. → Operations may not belong to the same cell.

Two operations for which the angle distance measurement between them is almost 90°. → Operations are independent and do not belong to the same cell (‘O2’ and O3 in Fig. 5).

If none of the above three cases are verified, the operation is not affected to any cell. → An exceptional operation. Since the objective is to group operations with minimum angle distance, Operation Oi, which has the smallest angle distance with Ok, is assigned to the operation group containing O_{i} and O_{ k }.
For illustrative problem two operation cells are identified having facility for operation ‘O1’, ‘O2’ and ‘O3’, ‘O4’, ‘O5’. The final clustered matrix would be same as Table 15. Further on the similar lines of Hachicha et al. (2008a) part may also be assigned through PCA.
Conclusions

It is computationally very simple and conceptually easy to understand.

It has the ability to consider a number of production and manufacturing flexibilityrelated data.

The relationship between the machines are found on the basis of commonality score.

The proposed CF procedure can also be implemented with any other compatible clustering algorithm.

It can be used for both cases, binary and nonbinary.

It minimizes the intercellular movement cost/time

It is adaptable for more sophisticated techniques like PCA, Taguchi’s method and others

Use of modern statistical and computational tools extend the applicability of proposed heuristic from mid to large size flexible manufacturing system.
Scope for further work, one hand lies in considerations to other/more production and manufacturing flexibility parameters like decisions on number of manufacturing cells and size, work imbalance, machine flexibility, etc. for the development of more realistic, efficient and effective simple CF procedure; on the other hand, in the development of simple procedures for simultaneous assignments of machine groups and part families.
Notes
Acknowledgments
The authors are indebted to the unknown reviewers for their critical review and the pointing suggestions that enabled us to bring out the present form of the work. We are, indeed, thankful to them.
References
 Ahi A, Aryanezhad MB, Ashtiani B, Makui A (2009) A novel approach to determine cell formation, intracellular machine layout and cell layout in the CMS problem based on TOPSIS method. Comput Oper Res 36(5):1478–1496. doi: 10.1016/j.cor.2008.02.012
 Ahmed MU, Ahmed NU, Nandkeolyar U (1991) A volume and material handling cost based heuristic for designing cellular manufacturing cells. J Oper Manag 10:488–511CrossRefGoogle Scholar
 Albadawi Z, Bashir HA, Chen M (2005) A mathematical approach for the formation of manufacturing cells. Comput Ind Eng 48:3–21CrossRefGoogle Scholar
 Arkat J, Farahani MH (2012) Integrating cell formation with cellular lay out and operations scheduling. Adv Int J manuf technol 61:637–647CrossRefGoogle Scholar
 Beaulieu A, AitKadi D, Gharbi A (1993) Heuristic for flexible machine selection problems. J Decis Syst 2:241–253. doi: 10.1080/12460125.1993.10511583 CrossRefGoogle Scholar
 Beaulieu A, Gharbi A, AitKadi (1997) An algorithm for the cell formation and the machine selection problems in the design of a cellular manufacturing system. Int J Prod Res 35(7):1857–1874. doi: 10.1080/002075497194958 CrossRefzbMATHGoogle Scholar
 Boutsinas B (2013). Machinepart cell formation using biclustering. Eur J Oper Res 230(3):563–572. doi: 10.1016/j.ejor.2013.05.007
 Chandrasekharan MP, Rajagopalan R (1986) An ideal seed nonhierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24:451–464CrossRefzbMATHGoogle Scholar
 Chattopadhyay M, Mazumdar S, Dan PK, Chakraborty PS (2012) Application of principal component analysis in machinepart cell formation. Manag Sci Lett 2:1175–1188CrossRefGoogle Scholar
 Doulabi SHH, Hojabri H, SeyedAlagheband S, Jaafari AA, Davoudpour H, (2009) Twophase approach for solving cellformation problem in cell manufacturing. In: Proceedings of the world congress on engineering and computer science 2009 Vol II WCECS 2009, October 20–22, 2009, San Francisco, USA, ISBN:9789881821027 WCECS 2009 Proceedings of the World Congress on EngineeringGoogle Scholar
 Elbenani B, Ferland JA (2012) Cell formation problem solved exactly with the dinkelbach algorithm. https://www.cirrelt.ca/DocumentsTravail/CIRRELT201207.pdf. Accessed 25.11.2013
 Eşme U (2009) Application of Taguchi method for the optimization of resistance spot welding process. Arab J Sci Eng 34(2B):519–528Google Scholar
 Fardis F, Zandi A, Ghezavati V (2013) Stochastic extension of cellular manufacturing systems: a queuingbased analysis. J Ind Eng Int 9:20CrossRefGoogle Scholar
 Garbie IH, Parsaei HR, Leep HR (2008) Machine cell formation based on a new similarity coefficient. J Ind Syst Eng 1(4):318–344Google Scholar
 Ghosh T, Dan PK (2011) Taguchi’s orthogonal design based soft computing methodology to solve cell formation problem on production shop floor. Acta Technica Corviniensis 4:81–87 ISSN 20673809Google Scholar
 Gupta A, Jain PK, Kumar D (2012) Formation of part family in reconfigurable manufacturing system using principle component analysis and Kmeans algorithm. In: Katalinic B (ed) Annals of DAAAM for 2012 and Proceedings of the 23rd International DAAAM Symposium, vol 23, 1st edn. DAAAM International, Vienna, AustriaGoogle Scholar
 Hachicha W, Masmoudi F, Haddar M (2006) A correlation analysis approach of cell formation in cellular manufacturing system with incorporated production data. Int J Manufac Res 1(3):332–353CrossRefGoogle Scholar
 Hachicha W, Masmoudi F, Haddar M (2008a) Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach. Int J Adv Manuf Technol 36:1157–1169. doi: 10.1007/s0017000709289 CrossRefGoogle Scholar
 Hachicha W, Masmoudi F, Haddar M (2008b) A Taguchi method application for the part routing selection in Generalized Group Technology: a case study. Munich Personal RePEc Archive. http://mpra.ub.unimuenchen.de/12376/. MPRA Paper No. 12376, posted 27. December 2008 15:12 UTC
 Hadighi SA, Sahebjamnia N, Mahdavi I, Asadollahpour H, Shafieian H (2013) MahalanobisTaguchi systembased criteria selection for strategy formulation: a case in a training institution. J Ind Eng Int 9:26CrossRefGoogle Scholar
 Kamaruddin S, Khan ZA, Wan KS (2004) The use of the Taguchi method in determining the optimum plastic injection moulding parameters for the production of a consumer product. Mekanikal 18:98–110Google Scholar
 Kia R, Shirazi H, Javadian N, TavakkoliMoghaddam R (2013) A multiobjective model for designing a group layout of a dynamic cellular manufacturing system. J Ind Eng Int 9:8CrossRefGoogle Scholar
 Kim CO, Baek JG, Baek JK (2004) A twophase heuristic algorithm for cell formation problems considering alternative part routes and machine sequences. Int J Prod Res 42(18):3911–3927. doi: 10.1080/00207540410001704078 CrossRefzbMATHGoogle Scholar
 Krushinsky D, Goldengorin B (2012) An exact model for cell formation in group technology. Comput Manag Sci 9:323–338. doi: 10.1007/s1028701201462 CrossRefzbMATHMathSciNetGoogle Scholar
 Kumar J, Jain PK (2008) Partmachine group formation with operation sequence, time, and production volume. Int J Simul Model 7(4):198–209. doi: 10.2507/IJSIMM07(4)4.113
 Kumar J, Jain PK (2010) Concurrently partmachine groups formation with important production data 9(1). Int J Simul Model 9(1):5–16 ISSN 17264529CrossRefGoogle Scholar
 Kumar S, Sharma RK (2014) Cell formation heuristic procedure considering production data. Int J Prod Manag Eng 2(2):75–84. doi: 10.4995/ijpme.2014.2078
 Leem C, Chen JJ (1996) Fuzzysetbased machinecell formation in cellular manufacturing. J Intell Manuf 7:355–364CrossRefGoogle Scholar
 Lian J, Liu C, Li W, Evans S, Yin Y (2013) Formation of independent manufacturing cells with the consideration of multiple identical machines. Int J Prod Res. doi: 10.1080/00207543.2013.843797 Google Scholar
 Llin A, Raiko T (2010) Practical approaches to principal component analysis in the presence of missing values. J Mach Learn Res 11:1957–2000MathSciNetGoogle Scholar
 Mahesh O, Srinivasan G (2002) Incremental cell formation considering alternative machines. Int J Prod Res 40(14):3291–3310. doi: 10.1080/00207540210146189 CrossRefzbMATHGoogle Scholar
 Masmoudi F, Hachicha W, Haddar M (2008) A new combined framework for the cellular manufacturing systems design. In: Proceedings of the 2008 international conference of manufacturing engineering and engineering management. LondonGoogle Scholar
 Mehrjoo S, Bashiri M (2013) An application of principal component analysis and logistic regression to facilitate production scheduling decision support system: an automotive industry case. J Ind Eng Int 9:14CrossRefGoogle Scholar
 Miltenburg J, Zhang W (1991) A comparative evaluation of nine wellknown algorithms for solving cell formation problem in group technology. J Oper Manag 10(1):44–72CrossRefGoogle Scholar
 Min Z, Alan WG, Shuguang H, Zhen HE (2014) Modified multivariate process capability index using principal component analysis. Chin J Mech Eng 27(2):249–259. doi: 10.3901/CJME.2014.02.249
 Mukattash AM, Adil MB, Tahboub KK (2002) Heuristic approaches for part assignment in cell formation. Comput Ind Eng 42:329–341CrossRefGoogle Scholar
 Murugan M, Selladurai V (2011) Formation of machine cells/part families in cellular manufacturing systems using an ARTmodified single linkage clustering approach—a comparative study. Jordan J Mech Ind Eng 5(3):199–212Google Scholar
 Muruganandam A, Prabhakaran G, Murali RV (2008) PRABHA—a new heuristic approach for machine cell formation under dynamic production environments. Int J Appl Manag Technol 6(3):191–221Google Scholar
 Nair GJ, Narendran TT (1998) CASE: a clustering algorithm for cell formation with sequence data. Int J Prod Res 36(1):157–180. doi: 10.1080/002075498193985 CrossRefzbMATHGoogle Scholar
 Nourie H, Tang SH, Tuah BTH, Ariffin MKA, Samin R (2013) Metaheuristic techniques on cell formation in cellular manufacturing system. J Autom Control Eng 1(1):49–54CrossRefGoogle Scholar
 Pandian RS, Mahapatra SS (2009) Manufacturing cell formation with production data using neural networks. Comput Ind Eng 56(4):1340–1347. doi: http://dx.doi.org/10.1016/j.cie.2008.08.003
 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. doi: 10.1016/j.ejor.2009.10.020
 Paydar MM, Sahebjamnia N (2009) Designing a mathematical model for cell formation problem using operation sequence. J Appl Oper Res 1(1):30–38Google Scholar
 Reisman A, Kumar A, Motwani J, Cheng CH (1997) Cellular manufacturing: a statistical review of the literature (1965–1995). Oper Res 45(4):508–520. doi: 10.1287/opre.45.4.508
 Saeedi S, Solimanpur M, Mahdavi I, Javadian N (2010) Heuristic approaches for cell formation in cellular manufacturing. J Softw Eng Appl 3:674–682. doi: 10.4236/jsea.2010.37077 CrossRefGoogle Scholar
 Sarker BR (1996) The resemblance coefficients in group technology: a survey and comparative study of relational metrics. Comput ind Eng 30(1):103–116CrossRefGoogle Scholar
 Seenivasan D, Selladurai V, Senthil P (2014) Optimization of liquid desiccant dehumidifier performance using Taguchi method. Adv Mech Eng 2014:1–6. Article ID 506487. doi: 10.1155/2014/506487
 Selim HM, Askin RG, Vakharia AJ (1998) Cell formation in group technology: review, evaluation and directions for future research. Comput Ind Eng 34(1):3–20CrossRefGoogle Scholar
 Sofianopoulou S (1999) Manufacturing cell design with alternative process plans and/or replicate machines. Int J Prod Res 37:707–720CrossRefzbMATHGoogle Scholar
 Susanto S, AlDabass D, Bhattacharya A (2009) Optimised cell formation algorithm considering sequence of operations, alternative routing and partvolume, 2009. Third Asia international conference on modelling and simulation. doi: 10.1109/AMS.2009.145
 Unal R, Dean EB (1991) Taguchi approach to design optimization for quality and cost: an overview. Presented at the 1991 annual conference of the international society of parametric analystsGoogle Scholar
 Wang J (2003) Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm. Automatica 39:1607–1615CrossRefzbMATHGoogle Scholar
 Wicks EM, Reasor RJ (1999) Designing manufacturing systems with dynamic part populations. IIE Trans 31:11–20Google Scholar
 Won Y, Lee KC (2001) Group technology cell formation considering operation sequences and production volumes. Int J Prod Res 39(13):2755–2768. doi: 10.1080/00207540010005060 CrossRefzbMATHGoogle Scholar
 Yasuda K, Hu L, Yin Y (2005) A grouping genetic algorithm for multiobjective cell formation problem. Int J Prod Res 43(4):829–853. doi: 10.1080/00207540512331311859
 Yin Y, Yasuda K (2006) Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Comput Ind Eng 48:471–489CrossRefGoogle Scholar
Copyright information
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.