Abstract
The cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) has been presented and computational results have been obtained for the proposed scheme with a set of 52 benchmark instances collected from literature. The results presented herein demonstrate that overall proposed HCSA is much more promising in comparison with existing approaches available in recent literatures. Extensive statistical and convergence tests have been carried out to ratify the superiority of the proposed HCSA. The improvements can be attributed to the collaborative interactions in the CSA mechanism, the proposed hybridization for initial population generation and so forth.
Similar content being viewed by others
References
Abdel-Basset M, Fakhry AE, El-Henawy I, Qiu T, Sangaiah AK (2017a) Feature and intensity based medical image registration using particle swarm optimization. J Med Syst 41(12):197
Abdel-Basset M, El-Shahat D, Sangaiah AK (2017b) A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem. Int J Mach Learn Cybern 1:1–20
Abdel-Basset M, El-Shahat D, El-Henawy I, Sangaiah AK (2018a) A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making. Soft Comput 22(13):4221–4239
Abdel-Basset M, El-Shahat D, El-henawy I, Sangaiah AK, Ahmed SH (2018b) A novel whale optimization algorithm for cryptanalysis in Merkle–Hellman cryptosystem. Mob Netw Appl 5:1–11
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
Albadawi Z, Bashir HA, Chen M (2005) A mathematical approach for the formation of manufacturing cells. Comput Ind Eng 48(1):3–21
Arikan F, Güngör Z (2005) A parametric model for cell formation and exceptional elements’ problems with fuzzy parameters. J Intell Manuf 16(1):103–114
Arkat J, Hosseini L, Farahani MH (2011) Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm. Expert Syst Appl 38(8):9597–9602
Arkat J, Abdollahzadeh H, Ghahve H (2012) A new branch and bound algorithm for cell formation problem. Appl Math Model 36(10):5091–5100
Asktn RG, Subramantan SP (1987) A cost-based heuristic for group technology configuration. Int J Prod Res 25(1):101–113
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
Balakrishnan SM, Sangaiah AK (2017a) MIFIM—middleware solution for service centric anomaly in future internet models. Future Gener Comput Syst 74:349–365
Balakrishnan SM, Sangaiah AK (2017b) Integrated QoUE and QoS approach for optimal service composition selection in internet of services (IoS). Multimed Tools Appl 76(21):22889–22916
Batsyn M, Bychkov I, Goldengorin B, Pardalos P, Sukhov P (2013) Pattern-based heuristic for the cell formation problem in group technology. In: Goldengorin B (ed) Models, algorithms, and technologies for network analysis, pp 11–50. Springer, New York
Boctor FF (1991) A linear formulation of the machine-part cell formation problem. Int J Prod Res 29(2):343–356
Boe WJ, Cheng CH (1991) A close neighbour algorithm for designing cellular manufacturing systems. Int J Prod Res 29(10):2097–2116
Burbidge JL (1971) Production flow analysis. Prod Eng 50(4.5):139–152
Bychkov I, Batsyn M (2018) An efficient exact model for the cell formation problem with a variable number of production cells. Comput Oper Res 91:112–120
Cai X, Gao XZ, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Int J Bio-Inspir Comput 8(4):205–214
Cai X, Wang H, Cui Z, Cai J, Xue Y, Wang L (2018) Bat algorithm with triangle-flipping strategy for numerical optimization. Int J Mach Learn Cybern 9(2):199–215
Carrie AS (1973) Numerical taxonomy applied to group technology and plant layout. Int J Prod Res 11(4):399–416
Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular manufacture. J Manuf Syst 1(1):65–75
Chan FT, Lau KW, Chan PL, Choy KL (2006) Two-stage approach for machine-part grouping and cell layout problems. Robot Comput Integr Manuf 22(3):217–238
Chandrasekharan M, Rajagopalan R (1986a) MODROC: an extension of rank order clustering for group technology. Int J Prod Res 24(5):1221–1233
Chandrasekharan PM, Rajagopalan R (1986b) An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. Int J Prod Res 24(2):451–463
Chandrasekharan MP, Rajagopalan R (1987) ZODIAC—an algorithm for concurrent formation of part-families and machine-cells. Int J Prod Res 25(6):835–850
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
Chattopadhyay M, Dan PK, Mazumdar S (2012) Application of visual clustering properties of self organizing map in machine-part cell formation. Appl Soft Comput 12(2):600–610
Chaudhuri B, Jana RK, Sharma DK, Dan PK (2019) A goal programming embedded genetic algorithm for multi-objective manufacturing cell design. Int J Appl Decis Sci 12(1):98–114
Chen SJ, Cheng CS (1995) A neural network-based cell formation algorithm in cellular manufacturing. Int J Prod Res 33(2):293–318
Chiang HS, Sangaiah AK, Chen MY, Liu JY (2018) A novel artificial bee colony optimization algorithm with SVM for bio-inspired software-defined networking. Int J Parallel Program 1:1–19
Chu CH (1997) An improved neural network for manufacturing cell formation. Decis Support Syst 20(4):279–295
Cui Z, Sun B, Wang G, Xue Y, Chen J (2017) A novel oriented cuckoo search algorithm to improve DV-hop performance for cyber–physical systems. J Parallel Distrib Comput 103:42–52
Cui Z, Cao Y, Cai X, Cai J, Chen J (2018) Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things. J Parallel Distrib Comput. https://doi.org/10.1016/j.jpdc.2017.12.014
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
De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput 6(3):239–251
Deb K (2001) Multi-objective optimization using evolutionary algorithms, vol 16. Wiley, New York
Delgoshaei A, Gomes C (2016) A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Appl Soft Comput 49:27–55
Dimopoulos C, Zalzala AM (2000) Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans Evolut Comput 4(2):93–113
Floreano D, Mattiussi C (2008) Bio-inspired artificial intelligence: theories, methods, and technologies. MIT Press, London
Gonçalves JF, Resende MG (2004) An evolutionary algorithm for manufacturing cell formation. Comput Ind Eng 47(2):247–273
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):377–382
Ham I, Hitomi K, Yoshida T (1985) Group technology: applications to production management. Kluwer Academic Publications, Alphen aan den Rijn
Hwang H, Sun JU (1996) A genetic-algorithm-based heuristic for the GT cell formation problem. Comput Ind Eng 30(4):941–955
Imran M, Kang C, Lee YH, Jahanzaib M, Aziz H (2017) Cell formation in a cellular manufacturing system using simulation integrated hybrid genetic algorithm. Comput Ind Eng 105:123–135
James TL, Brown EC, Keeling KB (2007) A hybrid grouping genetic algorithm for the cell formation problem. Comput Oper Res 34(7):2059–2079
Karoum B, Elbenani B, El Khattabi N, El Imrani AA (2018) Manufacturing cell formation problem using hybrid Cuckoo search algorithm. In: Amodeo L (ed) Recent developments in metaheuristics, pp 151–162. Springer, Cham
Kim J, Bentley PJ (2001) Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 congress on evolutionary computation, IEEE, vol 2, pp 1244–1252
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
King JR, Nakornchai V (1982) Machine-component group formation in group technology: review and extension. Int J Prod Res 20(2):117–133
Kumar KR, Vannelli A (1986) Strategic subcontracting for efficient disaggregated manufacturing. BEBR faculty working paper; no. 1252
Kumar KR, Kusiak A, Vannelli A (1986) Grouping of parts and components in flexible manufacturing systems. Eur J Oper Res 24(3):387–397
Kusiak A, Cho M (1992) Similarity coefficient algorithms for solving the group technology problem. Int J Prod Res 30(11):2633–2646
Kusiak A, Chow WS (1987) Efficient solving of the group technology problem. J Manuf Syst 6(2):117–124
Li X, Baki MF, Aneja YP (2010) An ant colony optimization metaheuristic for machine-part cell formation problems. Comput Oper Res 37(12):2071–2081
Lin TL, Dessouky MM, Kumar KR, Ng SM (1996) A heuristic-based procedure for the weighted production-cell formation problem. IIE Trans 28(7):579–590
Lu X, Tang K, Sendhoff B, Yao X (2014) A review of concurrent optimisation methods. Int J Bio-Inspir Comput 6(1):22–31
Mahdavi I, Paydar MM, Solimanpur M, Heidarzade A (2009) Genetic algorithm approach for solving a cell formation problem in cellular manufacturing. Expert Syst Appl 36(3):6598–6604
Mahmoodian V, Jabbarzadeh A, Rezazadeh H, Barzinpour F (2017) A novel intelligent particle swarm optimization algorithm for solving cell formation problem. Neural Comput Appl 1:1–15
Martinez WL, Martinez AR (2007) Computational statistics handbook with MATLAB, vol 22. CRC Press, London
McAuley J (1972) Machine grouping for efficient production. Prod Eng 51:53–57
McCormick WT Jr, Schweitzer PJ, White TW (1972) Problem decomposition and data reorganization by a clustering technique. Oper Res 20(5):993–1009
Mitranov SP (1959) The scientific principles of group technology. National Lending Library, London
Mosier C, Taube L (1985a) The facets of group technology and their impacts on implementation—a state-of-the-art survey. Omega 13(5):381–391
Mosier C, Taube L (1985b) Weighted similarity measure heuristics for the group technology machine clustering problem. Omega 13(6):577–579
Nalluri MR, Roy DS (2017) Hybrid disease diagnosis using multiobjective optimization with evolutionary parameter optimization. J Healthc Eng 2017:27
Nedjah N, Mourelle LDM (2015) Evolutionary multi-objective optimisation: a survey. Int J Bio-Inspir Comput 7(1):1–25
Noktehdan A, Seyedhosseini S, Saidi-Mehrabad M (2016) A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy. Int J Adv Manuf Technol 82(1–4):25–37
Nouri H (2016) Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. Appl Math Model 40(2):1514–1531
Oliveira S, Ribeiro JFF, Seok SC (2008) A comparative study of similarity measures for manufacturing cell formation. J Manuf Syst 27(1):19–25
Oliveira S, Ribeiro JFF, Seok SC (2009) A spectral clustering algorithm for manufacturing cell formation. Comput Ind Eng 57(3):1008–1014
Onwubolu GC, Mutingi M (2001) A genetic algorithm approach to cellular manufacturing systems. Comput Ind Eng 39(1):125–144
Pailla A, Trindade AR, Parada V, Ochi LS (2010) A numerical comparison between simulated annealing and evolutionary approaches to the cell formation problem. Expert Syst Appl 37(7):5476–5483
Pandian RS, Mahapatra SS (2009) Manufacturing cell formation with production data using neural networks. Comput Indus Eng 56(4):1340–1347
Panigrahi BK, Yadav SR, Agrawal S, Tiwari MK (2007) A clonal algorithm to solve economic load dispatch. Electr Power Syst Res 77(10):1381–1389
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
Pinheiro RGS, Martins IC, Protti F, Ochi LS (2017) A matheuristic for the cell formation problem. Opt Lett 12:1–12
Rajesh KD, Krishna MM, Ali MA, Chalapathi PV (2017) A modified hybrid similarity coefficient based method for solving the cell formation problem in cellular manufacturing system. Mater Today Proc 4(2):1469–1477
Rao NM, Kannan K, Gao XZ, Roy DS (2018) Novel classifiers for intelligent disease diagnosis with multi-objective parameter evolution. Comput Electr Eng 67:483–496
Saeidi S, Solimanpur M, Mahdavi I, Javadian N (2014) A multi-objective genetic algorithm for solving cell formation problem using a fuzzy goal programming approach. Int J Adv Manuf Technol 70(9–12):1635–1652
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):215–236
Sahin YB, Alpay S (2016) A metaheuristic approach for a cubic cell formation problem. Expert Syst Appl 65:40–51
Sakhaii M, Tavakkoli-Moghaddam R, Bagheri M, Vatani B (2016) A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Appl Math Model 40(1):169–191
Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. No. AFIT/CI/CIA-95-039. Air force inst of tech Wright–Patterson AFB OH
Seifoddini HK (1989) Single linkage versus average linkage clustering in machine cells formation applications. Comput Ind Eng 16(3):419–426
Seifoddini H, Wolfe PM (1986) Application of the similarity coefficient method in group technology. IIE Trans 18(3):271–277
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
Selim HM, Abdelaal RM, Mahdi AI (2003) Formation of machine groups and part families: a modified SLC method and comparative study. Integr Manuf Syst 14(2):123–137
Shiyas CR, Pillai VM (2014) A mathematical programming model for manufacturing cell formation to develop multiple configurations. J Manuf Syst 33(1):149–158
Solimanpur M, Vrat P, Shankar R (2004) A multi-objective genetic algorithm approach to the design of cellular manufacturing systems. Int J Prod Res 42(7):1419–1441
Srikanth K, Panwar LK, Panigrahi BK, Herrera-Viedma E, Sangaiah AK, Wang GG (2018) Meta-heuristic framework: quantum inspired binary grey wolf optimizer for unit commitment problem. Comput Electr Eng 70:243–260
Srinlvasan 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
Stanfel LE (1985) Machine clustering for economic production. Eng Costs Prod Econ 9(1–3):73–81
Su CT, Hsu CM (1998) Multi-objective machine-part cell formation through parallel simulated annealing. Int J Prod Res 36(8):2185–2207
Suresh Kumar C, Chandrasekharan MP (1990) Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int J Prod Res 28(2):233–243
Tsai CC, Lee CY (2006) Optimization of manufacturing cell formation with a multi-functional mathematical programming model. Int J Adv Manuf Technol 30(3–4):309–318
Van Veldhuizen, DA (1999). Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. No. AFIT/DS/ENG/99-01. Air Force Institute of Technology Wright–Patterson AFB Oh School of Engineering
Verma A, Kaushal S, Sangaiah AK (2017) Computational intelligence based heuristic approach for maximizing energy efficiency in internet of things. In: Sangaiah AK (ed) Intelligent decision support systems for sustainable computing, pp 53–76. Springer, Cham
Waghodekar PH, Sahu S (1984) Machine-component cell formation in group technology: MACE. Int J Prod Res 22(6):937–948
Wemmerlöv U, Hyer NL (1987) Research issues in cellular manufacturing. Int J Prod Res 25(3):413–431
Wemmerlov U, Johnson DJ (1997) Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int J Prod Res 35(1):29–49
White JA, Garrett SM (2003) Improved pattern recognition with artificial clonal selection? In: Artificial immune systems, pp 181–193. Springer, Berlin
Wilcoxon Frank (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83
Wu TH, Chung SH, Chang CC (2010) A water flow-like algorithm for manufacturing cell formation problems. Eur J Oper Res 205(2):346–360
Yang MS, Yang JH (2008) Machine-part cell formation in group technology using a modified ART1 method. Eur J Oper Res 188(1):140–152
Yang JH, Sun L, Lee HP, Qian Y, Liang YC (2008) Clonal selection based memetic algorithm for job shop scheduling problems. J Bionic Eng 5(2):111–119
Yin Y, Yasuda K (2005) Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Comput Ind Eng 48(3):471–489
Zeb A, Khan M, Khan N, Tariq A, Ali L, Azam F, Jaffery SHI (2016) Hybridization of simulated annealing with genetic algorithm for cell formation problem. Int J Adv Manuf Technol 86(5–8):2243–2254
Zhang M, Wang H, Cui Z, Chen J (2018) Hybrid multi-objective cuckoo search with dynamical local search. Memet Comput 10(2):199–208
Zheng Z, Saxena N, Mishra KK, Sangaiah AK (2018) Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications. Future Gener Comput Syst 5:256
Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, ETH Zurich, Switzerland
Acknowledgement
Madhusudana Rao Nalluri and K. Kannan gratefully acknowledge Tata Realty-IT city-SASTRA Srinivasa Ramanujan Research Cell of our university (India) for the financial support extended to us in carrying out this research work. Xiao-Zhi Gao's research work was partially supported by the National Natural Science Foundation of China (NSFC) under Grant 51875113.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Nalluri, M.R., Kannan, K., Gao, XZ. et al. An efficient hybrid meta-heuristic approach for cell formation problem. Soft Comput 23, 9189–9213 (2019). https://doi.org/10.1007/s00500-019-03798-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-019-03798-7