Cell Production System Design: A Literature Review
DOI:
https://doi.org/10.52547/ijimes.1.1.16DOR:
https://dorl.net/dor/20.1001.1.27832678.2021.1.1.2.2Keywords:
Cell Production System, Cell Production System Design, Meta-heuristic AlgorithmsAbstract
Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design.
Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.
Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.
Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed.
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References
Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(2), 241-261. Doi: https://doi.org/10.1016/j.jmsy.2013.12.007
Askin, R. G. (2013). Contributions to the design and analysis of cellular manufacturing systems. International Journal of Production Research, 51(23-24), 6778-6787. Doi: https://doi.org/10.1080/00207543.2013.825745
Nouri, H. A., Leman, Z., Moghadam, H. P., & Sulaiman, R. (2014). Literature review on machine reliability in cellular manufacturing system. Am J Appl Sci, 11(12), 1964-1968.
Wu, L., Zhao, Y., Feng, Y., Niu, B., & Xu, X. (2021). Minimizing makespan of stochastic customer orders in cellular manufacturing systems with parallel machines. Computers & Operations Research, 125, 105101. Doi: https://doi.org/10.1016/j.cor.2020.105101
Zhao, Y., Xu, X., & Li, H. (2018). Minimizing expected cycle time of stochastic customer orders through bounded multi-fidelity simulations. IEEE Transactions on Automation Science and Engineering, 15(4), 1797-1809. doi: 10.1109/TASE.2018.2796090
Nasiri, M. M., & Naseri, F. (2019). Metaheuristic algorithms for the generalised cell formation problem considering machine reliability. International Journal of Process Management and Benchmarking, 9(4), 469-484. Doi: https://doi.org/10.1504/IJPMB.2019.103426
Zhao, Y., Xu, X., Li, H., & Liu, Y. (2018). Stochastic customer order scheduling with setup times to minimize expected cycle time. International Journal of Production Research, 56(7), 2684-2706. Doi: https://doi.org/10.1080/00207543.2017.1381348
Chen, J., Wang, M., Kong, X. T., Huang, G. Q., Dai, Q., & Shi, G. (2019). Manufacturing synchronization in a hybrid flowshop with dynamic order arrivals. Journal of Intelligent Manufacturing, 30(7), 2659-2668. Doi: https://doi.org/10.1007/s10845-017-1295-5
Aalaei, A., Kayvanfar, V., & Davoudpour, H. (2019). Integrating multi-dynamic virtual cellular manufacturing systems into multi-market allocation and production planning. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(2), 643-664. Doi: https://doi.org/10.1177/0954405417731465
Xue, G., & Offodile, O. F. (2020). Integrated optimization of dynamic cell formation and hierarchical production planning problems. Computers & Industrial Engineering, 139, 106155. Doi: https://doi.org/10.1016/j.cie.2019.106155
Dehnavi-Arani, S., Sadegheih, A., Mehrjerdi, Y. Z., & Honarvar, M. (2020). A new bi-objective integrated dynamic cell formation and AGVs’ dwell point location problem on the inter-cell unidirectional single loop. Soft Computing, 24(21), 16021-16042. Doi: https://doi.org/10.1007/s00500-020-04921-9
Nouri, F., Samadzad, S., & Nahr, J. (2019). Meta-heuristics algorithm for two-machine no-wait flow-shop scheduling problem with the effects of learning. Uncertain Supply Chain Management, 7(4), 599-618. Doi: 10.5267/j.uscm.2019.5.002
Yan, P., Liu, S. Q., Sun, T., & Ma, K. (2018). A dynamic scheduling approach for optimizing the material handling operations in a robotic cell. Computers & Operations Research, 99, 166-177. Doi: https://doi.org/10.1016/j.cor.2018.05.009
Rostami, A., Paydar, M. M., & Asadi-Gangraj, E. (2020). A hybrid genetic algorithm for integrating virtual cellular manufacturing with supply chain management considering new product development. Computers & Industrial Engineering, 145, 106565. Doi: https://doi.org/10.1016/j.cie.2020.106565
Alimian, M., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2020). New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems. Journal of Manufacturing Systems, 56, 341-358. Doi: https://doi.org/10.1016/j.jmsy.2020.06.011
Raoofpanah, H., Ghezavati, V., & Tavakkoli-Moghaddam, R. (2019). Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition. Engineering Optimization, 51(7), 1229-1250. Doi: https://doi.org/10.1080/0305215X.2018.1517258
Ghahremani-Nahr, J., Nozari, H., & Najafi, S. E. (2020). Design a green closed loop supply chain network by considering discount under uncertainty. Journal of Applied Research on Industrial Engineering, 7(3), 238-266. Doi: 10.22105/jarie.2020.251240.1198
Ghahremani Nahr, J., Ghodratnama, A., IzadBakhah, H. R., & Tavakkoli Moghaddam, R. (2019). Design of multi-objective multi-product multi period green supply chain network with considering discount under uncertainty. Journal of Industrial Engineering Research in Production Systems, 6(13), 119-137. Doi: 10.22084/ier.2017.8877.1421
Kia, R. (2020). A genetic algorithm to integrate a comprehensive dynamic cellular manufacturing system with aggregate planning decisions. International Journal of Management Science and Engineering Management, 15(2), 138-154. Doi: https://doi.org/10.1080/17509653.2019.1655674
Feng, H., Xia, T., Da, W., Xi, L., & Pan, E. (2019). Concurrent design of cell formation and scheduling with consideration of duplicate machines and alternative process routings. Journal of Intelligent Manufacturing, 30(1), 275-289. Doi: https://doi.org/10.1007/s10845-016-1245-7
Chen, X., An, Y., Zhang, Z., & Li, Y. (2020). An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. Journal of Manufacturing Systems, 54, 227-241. Doi: https://doi.org/10.1016/j.jmsy.2019.12.004
Kataoka, T. (2020). A multi-period mixed integer programming model on reconfigurable manufacturing cells. Procedia Manufacturing, 43, 231-238. Doi: https://doi.org/10.1016/j.promfg.2020.02.147
Sadeghi, A., Suer, G., Sinaki, R. Y., & Wilson, D. (2020). Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis. Computers & Industrial Engineering, 141, 106282. Doi: https://doi.org/10.1016/j.cie.2020.106282
Hong, Z., Zeng, Z., & Gao, L. (2021). Energy-efficiency scheduling of multi-cell manufacturing system considering total handling distance and eligibility constraints. Computers & Industrial Engineering, 151, 106998. Doi: https://doi.org/10.1016/j.cie.2020.106998
Tayal, A., Solanki, A., & Singh, S. P. (2020). Integrated frame work for identifying sustainable manufacturing layouts based on big data, machine learning, meta-heuristic and data envelopment analysis. Sustainable Cities and Society, 62, 102383. Doi: https://doi.org/10.1016/j.scs.2020.102383
Mourtzis, D., Siatras, V., Synodinos, G., Angelopoulos, J., & Panopoulos, N. (2020). A framework for adaptive scheduling in cellular manufacturing systems. Procedia CIRP, 93, 989-994. Doi: https://doi.org/10.1016/j.procir.2020.04.040
Duffner, F., Mauler, L., Wentker, M., Leker, J., & Winter, M. (2021). Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs. International Journal of Production Economics, 232, 107982. Doi: https://doi.org/10.1016/j.ijpe.2020.107982
Salimpour, S., Pourvaziri, H., & Azab, A. (2021). Semi-robust layout design for cellular manufacturing in a dynamic environment. Computers & Operations Research, 133, 105367. Doi: https://doi.org/10.1016/j.cor.2021.105367
Ebrahimi, H., Kianfar, K., & Bijari, M. (2021). Scheduling a cellular manufacturing system based on price elasticity of demand and time-dependent energy prices. Computers & Industrial Engineering, 159, 107460. Doi: https://doi.org/10.1016/j.cie.2021.107460
Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, 116, 454-471. Doi: https://doi.org/10.1016/j.eswa.2018.09.027
Adinarayanan, A., Dinesh, S., Balaji, D. S., & Umanath, K. (2021). Design of machine cell in cellular manufacturing systems using PSO approach. Materials Today: Proceedings. Doi: https://doi.org/10.1016/j.matpr.2021.02.472
Saraçoğlu, İ., Süer, G. A., & Gannon, P. (2021). Minimizing makespan and flowtime in a parallel multi-stage cellular manufacturing company. Robotics and Computer-Integrated Manufacturing, 72, 102182. Doi: https://doi.org/10.1016/j.rcim.2021.102182
Weeber, M., Wanner, J., Schlegel, P., Birke, K. P., & Sauer, A. (2020). Methodology for the simulation based energy efficiency assessment of battery cell manufacturing systems. Procedia Manufacturing, 43, 32-39. Doi: https://doi.org/10.1016/j.promfg.2020.02.179
McCormick Jr, W. T., Schweitzer, P. J., & White, T. W. (1972). Problem decomposition and data reorganization by a clustering technique. Operations Research, 20(5), 993-1009. Doi: https://doi.org/10.1287/opre.20.5.993
King, J. R. (1980). Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. International Journal of Production Research, 18(2), 213-232. Doi: https://doi.org/10.1080/00207548008919662
Kusiak, A. (1987). The generalized group technology concept. International journal of production research, 25(4), 561-569. Doi: https://doi.org/10.1080/00207548708919861
Li, Y., Liu, C., Zhang, L., & Sun, B. (2021). A partition optimization design method for a regional integrated energy system based on a clustering algorithm. Energy, 219, 119562. Doi: https://doi.org/10.1016/j.energy.2020.119562
Vialetto, G., & Noro, M. (2020). An innovative approach to design cogeneration systems based on big data analysis and use of clustering methods. Energy Conversion and Management, 214, 112901. Doi: https://doi.org/10.1016/j.enconman.2020.112901
McAuley, J. (1972). Machine grouping for efficient production. Production engineer, 51(2), 53-57. Doi:
Seifoddini, H. (1989). A note on the similarity coefficient method and the problem of improper machine assignment in group technology applications. The international journal of production research, 27(7), 1161-1165. Doi: https://doi.org/10.1080/00207548908942614
Gupta, T., & Seifoddini, H. I. (1990). Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. The international journal of production research, 28(7), 1247-1269. Doi: https://doi.org/10.1080/00207549008942791
Erenay, B., Suer, G. A., Huang, J., & Maddisetty, S. (2015). Comparison of layered cellular manufacturing system design approaches. Computers & Industrial Engineering, 85, 346-358. Doi: https://doi.org/10.1016/j.cie.2015.02.021
Wu, C., Peng, Q., Lee, J., Leibnitz, K., & Xia, Y. (2021). Effective hierarchical clustering based on structural similarities in nearest neighbor graphs. Knowledge-Based Systems, 107295. Doi: https://doi.org/10.1016/j.knosys.2021.107295
Wu, C., Li, H., & Ren, J. (2021). Research on hierarchical clustering method based on partially-ordered Hasse graph. Future Generation Computer Systems. Doi: https://doi.org/10.1016/j.future.2021.07.025
Selim, H. M., Askin, R. G., & Vakharia, A. J. (1998). Cell formation in group technology: review, evaluation and directions for future research. Computers & Industrial Engineering, 34(1), 3-20. Doi: https://doi.org/10.1016/S0360-8352(97)00147-2
Lemoine, Y., Mutel, B. (1983) Automatic recognition of production cells and part families. In advances in CAD/CAM, by T: M., Semenkov, O.I. Ellis, 239-248. Amsterdam: North-Holland.
Chandrasekharan, M., & Rajagopalan, R. (1986). MODROC: an extension of rank order clustering for group technology. International Journal of Production Research, 24(5), 1221-1233. Doi: https://doi.org/10.1080/00207548608919798
Srinivasan, G., & Narendran, T. T. (1991). GRAFICS—a nonhierarchical clustering algorithm for group technology. The International Journal of Production Research, 29(3), 463-478. Doi: https://doi.org/10.1080/00207549108930083
Nair, G. J., & Narendran, T. T. (1998). CASE: A clustering algorithm for cell formation with sequence data. International journal of production research, 36(1), 157-180. Doi: https://doi.org/10.1080/002075498193985
Park, J., Park, K. V., Yoo, S., Choi, S. O., & Han, S. W. (2020). Development of the WEEE grouping system in South Korea using the hierarchical and non-hierarchical clustering algorithms. Resources, Conservation and Recycling, 161, 104884. Doi: https://doi.org/10.1016/j.resconrec.2020.104884
Rajagopalan, R., & Batra, J. L. (1975). Design of cellular production systems a graph-theoretic approach. The International Journal of Production Research, 13(6), 567-579. Doi: https://doi.org/10.1080/00207547508943029
Witte, J. D. (1980). The use of similarity coefficients in production flow analysis. International Journal of Production Research, 18(4), 503-514. Doi: https://doi.org/10.1080/00207548008919686
Askin, R. G., & Chiu, K. S. (1990). A graph partitioning procedure for machine assignment and cell formation in group technology. The International Journal of Production Research, 28(8), 1555-1572. Doi: https://doi.org/10.1080/00207549008942812
Faber, Z., & Carter, M. W. (1986). A new graph theory approach for forming machine cells in cellular production systems. Flexible manufacturing systems: Methods and studies, 301-318.
Chen, H., Pan, X., Lu, X., & Xie, Q. (2020). A modified graph cuts image segmentation algorithm with adaptive shape constraints and its application to computed tomography images. Biomedical Signal Processing and Control, 62, 102092. Doi: https://doi.org/10.1016/j.bspc.2020.102092
Luo, W., Greene, A. S., & Constable, R. T. (2021). Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain. NeuroImage, 240, 118332. Doi: https://doi.org/10.1016/j.neuroimage.2021.118332
Sangeetha, S., Hema, P., Selvarani, N., Geetha, P., Karthikeyan, P., & Kumar, S. G. (2021). Fuzzy graph theory in coloring graph. Materials Today: Proceedings. Doi: https://doi.org/10.1016/j.matpr.2021.01.797
King, J. R., & Nakornchai, V. (1982). Machine-component group formation in group technology: review and extension. The international journal of production research, 20(2), 117-133. Doi: https://doi.org/10.1080/00207548208947754
Yıldırım, M., Okay, F. Y., & Özdemir, S. (2021). Big data analytics for default prediction using graph theory. Expert Systems with Applications, 176, 114840. Doi: https://doi.org/10.1016/j.eswa.2021.114840
Vohra, T., CHEN, D. S., CHANG, J. C., & CHEN, H. C. (1990). A network approach to cell formation in cellular manufacturing. International Journal of production research, 28(11), 2075-2084. Doi: https://doi.org/10.1080/00207549008942854
Lee, H., & Garcia-Diaz, A. (1996). Network flow procedures for the analysis of cellular manufacturing systems. IIE transactions, 28(4), 333-345. doi: https://doi.org/10.1080/07408179608966280
Bainy, R. G., Johnson, B. K., & Guzmán, A. (2021). Dynamic zone selection for busbar protection Using graph theory and path analysis. Electric Power Systems Research, 197, 107241. doi: https://doi.org/10.1016/j.epsr.2021.107241
Kusiak, A. (1988). EXGT-S: A knowledge based system for group technology. The International Journal Of Production Research, 26(5), 887-904. doi: https://doi.org/10.1080/00207548808947908
Luong, L., He, J., Abhary, K., & Qiu, L. (2002). A decision support system for cellular manufacturing system design. Computers & Industrial Engineering, 42(2-4), 457-470. doi: https://doi.org/10.1016/S0360-8352(02)00032-3
Xu, H., & Wang, H. P. (1989). Part family formation for GT applications based on fuzzy mathematics. The international Journal of production Research, 27(9), 1637-1651. doi: https://doi.org/10.1080/00207548908942644
Chu, C. H., & Hayya, J. C. (1991). A fuzzy clustering approach to manufacturing cell formation. The international journal of production research, 29(7), 1475-1487. doi: https://doi.org/10.1080/00207549108948024
Güngör, Z., & Arıkan, F. (2000). Application of fuzzy decision making in part-machine grouping. International Journal of Production Economics, 63(2), 181-193. doi: https://doi.org/10.1016/S0925-5273(99)00010-9
Josien, K., & Liao, T. W. (2002). Simultaneous grouping of parts and machines with an integrated fuzzy clustering method. Fuzzy Sets and Systems, 126(1), 1-21. doi: https://doi.org/10.1016/S0165-0114(01)00063-X
Lozano, S., Dobado, D., Larrañeta, J., & Onieva, L. (2002). Modified fuzzy C-means algorithm for cellular manufacturing. Fuzzy sets and systems, 126(1), 23-32. doi: https://doi.org/10.1016/S0165-0114(01)00003-3
Zolfagha Ri, S., & Liang, M. (1997). An objective-guided ortho-synapse Hopfield network approach to machine grouping problems. International Journal of Production Research, 35(10), 2773-2792. doi: https://doi.org/10.1080/002075497194444
Solimanpur, M., Saeedi, S., & Mahdavi, I. (2010). Solving cell formation problem in cellular manufacturing using ant-colony-based optimization. The International Journal of Advanced Manufacturing Technology, 50(9-12), 1135-1144. doi: 10.1007/s00170-010-2587-5.
Guerrero, F., Lozano, S., Smith, K. A., Canca, D., & Kwok, T. (2002). Manufacturing cell formation using a new self-organizing neural network. Computers & Industrial Engineering, 42(2-4), 377-382. doi: https://doi.org/10.1016/S0360-8352(02)00039-6
Saidi-Mehrabad, M., & Safaei, N. (2007). A new model of dynamic cell formation by a neural approach. The International Journal of Advanced Manufacturing Technology, 33(9-10), 1001-1009. doi: 10.1007/s00170-006-0518-2
Delgoshaei, A., & Gomes, C. (2016). A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost. Applied Soft Computing, 49, 27-55. doi: https://doi.org/10.1016/j.asoc.2016.06.025
Bulgak, A. A., & Bektas, T. (2009). Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration. European journal of operational research, 192(2), 414-428. doi: https://doi.org/10.1016/j.ejor.2007.09.023
Chan, F. T. S., Lau, K. W., Chan, L. Y., & Lo, V. H. Y. (2008). Cell formation problem with consideration of both intracellular and intercellular movements. International Journal of Production Research, 46(10), 2589-2620. doi: https://doi.org/10.1080/00207540500478843
Rogers, D. F., & Kulkarni, S. S. (2005). Optimal bivariate clustering and a genetic algorithm with an application in cellular manufacturing. European Journal of Operational Research, 160(2), 423-444. doi: https://doi.org/10.1016/j.ejor.2003.07.005
Lee-Post, A. (2000). Part family identification using a simple genetic algorithm. International journal of production research, 38(4), 793-810. doi: https://doi.org/10.1080/002075400189158
Mak, K. L., Wong, Y. S., & Wang, X. X. (2000). An adaptive genetic algorithm for manufacturing cell formation. The International Journal of Advanced Manufacturing Technology, 16(7), 491-497.
Wu, X., Chu, C. H., Wang, Y., & Yan, W. (2006). Concurrent design of cellular manufacturing systems: a genetic algorithm approach. International Journal of Production Research, 44(6), 1217-1241. doi: https://doi.org/10.1080/00207540500338252
Onwubolu, G. C., & Mutingi, M. (2001). A genetic algorithm approach to cellular manufacturing systems. Computers & industrial engineering, 39(1-2), 125-144. doi: https://doi.org/10.1016/S0360-8352(00)00074-7
Tariq, A., Hussain, I., & Ghafoor, A. (2009). A hybrid genetic algorithm for machine-part grouping. Computers & Industrial Engineering, 56(1), 347-356. doi: https://doi.org/10.1016/j.cie.2008.06.007
Joines, J. A., Culbreth, C. T., & King, R. E. (1996). Manufacturing cell design: an integer programming model employing genetic algorithms. IIE transactions, 28(1), 69-85. doi: https://doi.org/10.1080/07408179608966253
Solimanpur, M., Vrat, P., & Shankar, R. (2004). A multi-objective genetic algorithm approach to the design of cellular manufacturing systems. International journal of production research, 42(7), 1419-1441. doi: https://doi.org/10.1080/00207540310001638073
Kor, H., Iranmanesh, H., Haleh, H., & Hatefi, S. M. (2009, January). A multi-objective genetic algorithm for optimization of cellular manufacturing system. In 2009 International Conference on Computer Engineering and Technology (Vol. 1, pp. 252-256). IEEE. doi: 10.1109/ICCET.2009.212
Neto, A. R. P., & Gonçalves Filho, E. V. (2010). A simulation-based evolutionary multiobjective approach to manufacturing cell formation. Computers & Industrial Engineering, 59(1), 64-74. doi: https://doi.org/10.1016/j.cie.2010.02.017
Chandrasekar, K., & Venkumar, P. (2013). Genetic algorithm approach for integrating cell formation with machine layout and cell layout. International Journal of Operational Research, 16(2), 155-171. doi: https://doi.org/10.1504/IJOR.2013.051787
Wicks, E. M. (1995). Designing cellular manufacturing systems with time varying product mix and resource availability (Doctoral dissertation, Virginia Tech).
Izui, K., Murakumo, Y., Suemitsu, I., Nishiwaki, S., Noda, A., & Nagatani, T. (2013). Multiobjective layout optimization of robotic cellular manufacturing systems. Computers & Industrial Engineering, 64(2), 537-544. doi: https://doi.org/10.1016/j.cie.2012.12.003
Khaksar-Haghani, F., Kia, R., Mahdavi, I., & Kazemi, M. (2013). A genetic algorithm for solving a multi-floor layout design model of a cellular manufacturing system with alternative process routings and flexible configuration. The International Journal of Advanced Manufacturing Technology, 66(5), 845-865. doi: https://doi.org/10.1007/s00170-012-4370-2
Saxena, L. K., & Jain, P. K. (2011). Dynamic cellular manufacturing systems design—a comprehensive model. The International Journal of Advanced Manufacturing Technology, 53(1-4), 11-34. doi: 10.1007/s00170-010-2842-9
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. Applied Mathematical Modelling, 40(1), 169-191. doi: https://doi.org/10.1016/j.apm.2015.05.005
Suemitsu, I., Izui, K., Yamada, T., Nishiwaki, S., Noda, A., & Nagatani, T. (2016). Simultaneous optimization of layout and task schedule for robotic cellular manufacturing systems. Computers & Industrial Engineering, 102, 396-407. doi: https://doi.org/10.1016/j.cie.2016.05.036
Mohammadi, M., & Forghani, K. (2016). Designing cellular manufacturing systems considering S-shaped layout. Computers & Industrial Engineering, 98, 221-236. doi: https://doi.org/10.1016/j.cie.2016.05.041
Bootaki, B., Mahdavi, I., & Paydar, M. M. (2016). New criteria for configuration of cellular manufacturing considering product mix variation. Computers & Industrial Engineering, 98, 413-426. doi: https://doi.org/10.1016/j.cie.2016.06.021
Delgoshaei, A., Ali, A., Ariffin, M. K. A., & Gomes, C. (2016). A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty. Computers & Industrial Engineering, 100, 110-132. doi: https://doi.org/10.1016/j.cie.2016.08.010
Deep, K., & Singh, P. K. (2015). Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. Journal of Manufacturing Systems, 35, 155-163. doi: https://doi.org/10.1016/j.jmsy.2014.09.008
Azadeh, A., Pashapour, S., & Abdolhossein Zadeh, S. (2016). Designing a cellular manufacturing system considering decision style, skill and job security by NSGA-II and response surface methodology. International Journal of Production Research, 54(22), 6825-6847. doi: https://doi.org/10.1080/00207543.2016.1178407
Shirzadi, S., Tavakkoli-Moghaddam, R., Kia, R., & Mohammadi, M. (2017). A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system. International Journal of Computer Integrated Manufacturing, 30(8), 839-855. doi: https://doi.org/10.1080/0951192X.2016.1224388
Megala, N., Rajendran, C., & Gopalan, R. (2008). An ant colony algorithm for cell-formation in cellular manufacturing systems. European journal of industrial engineering, 2(3), 298-336. doi: https://doi.org/10.1504/EJIE.2008.017688
Li, X., Baki, M. F., & Aneja, Y. P. (2010). An ant colony optimization metaheuristic for machine–part cell formation problems. Computers & Operations Research, 37(12), 2071-2081. doi: https://doi.org/10.1016/j.cor.2010.02.007
Xing, B., Gao, W. J., Nelwamondo, F. V., Battle, K., & Marwala, T. (2010). Part-machine clustering: the comparison between adaptive resonance theory neural network and ant colony system. In Advances in Neural Network Research and Applications (pp. 747-755). Springer, Berlin, Heidelberg. doi: https://doi.org/10.1007/978-3-642-12990-2_87
Bajestani, M. A., Rabbani, M., Rahimi-Vahed, A. R., & Khoshkhou, G. B. (2009). A multi-objective scatter search for a dynamic cell formation problem. Computers & operations research, 36(3), 777-794. doi: https://doi.org/10.1016/j.cor.2007.10.026
Slomp, J., Chowdary, B. V., & Suresh, N. C. (2005). Design of virtual manufacturing cells: a mathematical programming approach. Robotics and Computer-Integrated Manufacturing, 21(3), 273-288. doi: https://doi.org/10.1016/j.rcim.2004.11.001
Arani, S. D., & Mehrabad, M. S. (2014). A two stage model for Cell Formation Problem (CFP) considering the inter-cellular movements by AGVs. Journal of Industrial and Systems Engineering, 7(1), 43-55. doi: 20.1001.1.17358272.2014.7.1.3.5
Delgoshaei, A., Ariffin, M. K. A., & Ali, A. (2017). A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS. International journal of production research, 55(4), 997-1039. doi: https://doi.org/10.1080/00207543.2016.1213445
Wu, T. H., Yeh, J. Y., & Chang, C. C. (2009). A hybrid tabu search algorithm to cell formation problem and its variants. World Academy of Science, Engineering and Technology, 53, 1090-1094.
Safaei, N., Saidi-Mehrabad, M., & Jabal-Ameli, M. S. (2008). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research, 185(2), 563-592. doi: https://doi.org/10.1016/j.ejor.2006.12.058
Defersha, F. M., & Chen, M. (2008). A parallel multiple Markov chain simulated annealing for multi-period manufacturing cell formation problems. The International Journal of Advanced Manufacturing Technology, 37(1-2), 140-156. doi: 10.1007/s00170-007-0947-6.
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 and Systems, 159(2), 215-236. doi: https://doi.org/10.1016/j.fss.2007.06.014
Safaei, N., & Tavakkoli-Moghaddam, R. (2009). An extended fuzzy parametric programming-based approach for designing cellular manufacturing systems under uncertainty and dynamic conditions. International Journal of Computer Integrated Manufacturing, 22(6), 538-548. doi: https://doi.org/10.1080/09511920802616773
Dalfard, V. M. (2013). New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements. Applied Mathematical Modelling, 37(4), 1884-1896. doi: https://doi.org/10.1016/j.apm.2012.04.034
Kia, R., Shirazi, H., Javadian, N., & Tavakkoli-Moghaddam, R. (2015). Designing group layout of unequal-area facilities in a dynamic cellular manufacturing system with variability in number and shape of cells. International Journal of Production Research, 53(11), 3390-3418. doi: https://doi.org/10.1080/00207543.2014.986295
Liu, C., & Wang, J. (2016). Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing. International Journal of Computational Intelligence Systems, 9(4), 765-777. doi: https://doi.org/10.1080/18756891.2016.1204123
Logendran, R., & Karim, Y. (2003). Design of manufacturing cells in the presence of alternative cell locations and material transporters. Journal of the Operational Research Society, 54(10), 1059-1075. doi: https://doi.org/10.1057/palgrave.jors.2601608
Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2012). A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system. Journal of Manufacturing Systems, 31(2), 214-223. doi: https://doi.org/10.1016/j.jmsy.2011.07.007
Foulds, L. R., French, A. P., & Wilson, J. M. (2006). The sustainable cell formation problem: manufacturing cell creation with machine modification costs. Computers & Operations Research, 33(4), 1010-1032. doi: https://doi.org/10.1016/j.cor.2004.09.001
Lei, D., & Wu, Z. (2006). Tabu search for multiple-criteria manufacturing cell design. The International Journal of Advanced Manufacturing Technology, 28(9), 950-956. doi: https://doi.org/10.1007/s00170-004-2441-8
Caprihan, R., Slomp, J., & Agarwal, K. (2009, December). A quantum particle swarm optimization approach for the design of virtual manufacturing cells. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 125-129). IEEE. doi: 10.1109/IEEM.2009.5373408
Anvari, M., Mehrabad, M. S., & Barzinpour, F. (2010). Machine–part cell formation using a hybrid particle swarm optimization. The International Journal of Advanced Manufacturing Technology, 47(5), 745-754. doi: https://doi.org/10.1007/s00170-009-2202-9
Durán, O., Rodriguez, N., & Consalter, L. A. (2010). Collaborative particle swarm optimization with a data mining technique for manufacturing cell design. Expert Systems with Applications, 37(2), 1563-1567. doi: https://doi.org/10.1016/j.eswa.2009.06.061
Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert systems with applications, 116, 454-471. doi: https://doi.org/10.1016/j.eswa.2018.09.027
Law, A. M., & McComas, M. G. (1998, December). Simulation of manufacturing systems. In 1998 Winter Simulation Conference. Proceedings (Cat. No. 98CH36274) (Vol. 1, pp. 49-52). IEEE. doi: 10.1109/WSC.1998.744898
Reeb, J. E., Baker, E. S., Brunner, C. C., Funck, J. W., & Reiter, W. F. (2010). Using simulation to select part-families for cell manufacturing. International Wood Products Journal, 1(1), 43-47. doi: https://doi.org/10.1179/002032010X12858356612744
Durmusoglu, M. B., & Satoglu, S. I. (2011). Axiomatic design of hybrid manufacturing systems in erratic demand conditions. International Journal of Production Research, 49(17), 5231-5261. doi: https://doi.org/10.1080/00207543.2010.510487
Siemiatkowski, M., & Przybylski, W. (2007). Modelling and simulation analysis of process alternatives in the cellular manufacturing of axially symmetric parts. The International Journal of Advanced Manufacturing Technology, 32(5), 516-530. doi: https://doi.org/10.1007/s00170-005-0366-5
Azadeh, A., Anvari, M., Ziaei, B., & Sadeghi, K. (2010). An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems. The International Journal of Advanced Manufacturing Technology, 46(1-4), 361-375. doi: 10.1007/s00170-009-2088-6
Azadeh, A., Nokhandan, B. P., Asadzadeh, S. M., & Fathi, E. (2011). Optimal allocation of operators in a cellular manufacturing system by an integrated computer simulation–genetic algorithm approach. International Journal of Operational Research, 10(3), 333-360. doi: https://doi.org/10.1504/IJOR.2011.038905
Ranaiefar, F., Mohagheghzadeh, R., Chitsaz, M., Ardakani, M. F., & Shahbazi, M. J. (2009, November). Material flow planning in cellular manufacturing systems by computer simulation. In 2009 Third UKSim European Symposium on Computer Modeling and Simulation (pp. 430-434). IEEE. doi: 10.1109/EMS.2009.43
Pitchuka, L. N., Adil, G. K., & Ananthakumar, U. (2006). Effect of conversion of functional layout to a cellular layout on the queue time performance: some new insights. The International Journal of Advanced Manufacturing Technology, 31(5-6), 594-601. doi: 10.1007/s00170-005-0219-2.
Chtourou H, Jerbi A, Maalej A. (2008) The cellular manufacturing paradox: a critical review of simulation studies. Journal of Manufacturing Technology Management, 19(5), 591–606. doi: https://doi.org/10.1108/17410380810877276
Nozari, H., Najafi, E., Fallah, M., & Hosseinzadeh Lotfi, F. (2019). Quantitative analysis of key performance indicators of green supply chain in FMCG industries using non-linear fuzzy method. Mathematics, 7(11), 1020. doi: https://doi.org/10.3390/math7111020
Vakharia, A. J., & Wemmerlov, U. (1990). Designing a cellular manufacturing system: a materials flow approach based on operation sequences. IIE transactions, 22(1), 84-97. Doi: https://doi.org/10.1080/07408179008964161
Alfa, A. S., Chen, M., & Heragu, S. S. (1992). Integrating the grouping and layout problems in cellular manufacturing systems. Computers & Industrial Engineering, 23(1-4), 55-58. Doi: https://doi.org/10.1016/0360-8352(92)90062-O
Balakrishnan, J., & Cheng, C. H. (2005). Dynamic cellular manufacturing under multiperiod planning horizons. Journal of manufacturing technology management. Doi: https://doi.org/10.1108/17410380510600491
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. Computers & operations research, 39(11), 2642-2658. Doi: https://doi.org/10.1016/j.cor.2012.01.012
Paydar, M. M., Mahdavi, I., Solimanpur, M., & Tajdin, A. (2008, December). Solving a new mathematical model for cellular manufacturing system: fuzzy goal programming. In 2008 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1224-1228). IEEE. doi: 10.1109/IEEM.2008.4738065.
Ahi, A., Aryanezhad, M. B., 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. Computers & Operations Research, 36(5), 1478-1496. Doi: https://doi.org/10.1016/j.cor.2008.02.012
Fardis, F., Zandi, A., & Ghezavati, V. (2013). Stochastic extension of cellular manufacturing systems: a queuing-based analysis. Journal of industrial engineering international, 9(1), 1-8. Doi: https://doi.org/10.1186/2251-712X-9-20
Safaei, N., & Tavakkoli-Moghaddam, R. (2009). Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems. International Journal of Production Economics, 120(2), 301-314. Doi: https://doi.org/10.1016/j.ijpe.2008.12.013
Eski, O., & Ozkarahan, I. (2007, August). Design of manufacturing cells for uncertain production requirements with presence of routing flexibility. In International Conference on Intelligent Computing (pp. 670-681). Springer, Berlin, Heidelberg. Doi: https://doi.org/10.1007/978-3-540-74205-0_71
Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation, 184(2), 721-728. Doi: https://doi.org/10.1016/j.amc.2006.05.172
Ghezavati, V. R., & Saidi-Mehrabad, M. (2011). An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis. Expert Systems with Applications, 38(3), 1326-1335. Doi: https://doi.org/10.1016/j.eswa.2010.07.012
Torabi, S. A., & Amiri, A. S. (2012). A possibilistic approach for designing hybrid cellular manufacturing systems. International Journal of Production Research, 50(15), 4090-4104. Doi: https://doi.org/10.1080/00207543.2011.590827
Arıkan, F., & Güngör, Z. (2009). Modeling of a manufacturing cell design problem with fuzzy multi-objective parametric programming. Mathematical and Computer Modelling, 50(3-4), 407-420. Doi: https://doi.org/10.1016/j.mcm.2009.04.017
Arıkan, F., & Güngör, Z. (2007). A two-phase approach for multi-objective programming problems with fuzzy coefficients. Information sciences, 177(23), 5191-5202. Doi: https://doi.org/10.1016/j.ins.2007.06.023
Mahdavi, I., Javadi, B., Fallah-Alipour, K., & Slomp, J. (2007). Designing a new mathematical model for cellular manufacturing system based on cell utilization. Applied mathematics and Computation, 190(1), 662-670. Doi: https://doi.org/10.1016/j.amc.2007.01.060
Tavakkoli-Moghaddam, R., Safaei, N., & Sassani, F. (2008). A new solution for a dynamic cell formation problem with alternative routing and machine costs using simulated annealing. Journal of the Operational Research Society, 59(4), 443-454. Doi: https://doi.org/10.1057/palgrave.jors.2602436
Eguia, I., Molina, J. C., Lozano, S., & Racero, J. (2017). Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing. International Journal of Production Research, 55(10), 2775-2790. Doi: https://doi.org/10.1080/00207543.2016.1193673
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