Designing Cell Production Arrangement Scenarios with the Approach of Artificial Neural Networks

Subject Areas : Business Strategy

Mahdi Ahmadipanah ¹ , Kamyar Chalaki ² , Roya Shakeri ³

1 - Ph.D. Candidate of Industrial Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran,
2 - Assistant Professor, Department of Industrial Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
3 - Assistant Professor, Department of Management, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Received: 2022-07-31 Accepted : 2022-10-15 Published : 2022-12-01

Keywords:

References:

• Al-Badi, R., Yousif, J., Kazem, H., Al-Balushi, H. (2022) Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules. International Journal of Energirs, 15(4), 146-161. doi.org/10.3390/en15114138


• Arkat, J., Saidi, M., & Abbasi, B. (2007). Applying simulated annealing to cellular manufacturing system design. The International Journal of Advanced Manufacturing Technology, 32(5), 531-536. org/10.1007/s00170-005-0358-5


• Asadbek, M. (2019). Genetic algorithm. Tehran: Asadzadeh.


• Balakrishnan, J., & Cheng, C. H. (2007). Multi-period planning and uncertainty issues in cellular manufacturing: A review and future directions. European journal of operational research, 177(1), 281-309. org/10.1016/j.ejor.2005.08.027


• Baronti, L., Michalek, A., Castellani, M., Penchev, P., Dimov, S. (2022). Artificial neural network tools for predicting the functional response of ultrafast laser textured/structured surfaces. The International Journal of Advanced Manufacturing Technology, (119), 3501–3516. doi.org/10.1007/s00170-021-08589-9


• Choobineh, F. (1988). A framework for the design of cellular manufacturing systems. The International Journal of Production Research, 26(7), 1161-1172. org/10.1080/00207548808947932


• Clark, L., (2022). Performance analysis of artificial neural network models for hour-ahead electric load forecasting. Journal of Procedia Computer Science, (197), 16-24. doi.org/10.1016/j.procs.2021.12.113


• Das, K., Lashkari, R. S., & Sengupta, S. (2007). Machine reliability and preventive maintenance planning for cellular manufacturing systems. European Journal of Operational Research, 183(1), 162-180. org/10.1016/j.ejor.2006.09.079


• Das, K., Lashkari, R. S., & Sengupta, S. (2007). Reliability consideration in the design and analysis of cellular manufacturing systems. International Journal of Production Economics, 105(1), 243-262. org/10.1016/j.ijpe.2006.04.015


• Fraser, K., Harris, H., & Luong, L. (2007). Improving the implementation effectiveness of cellular manufacturing: a comprehensive framework for practitioners. International Journal of Production Research, 45(24), 5835-5856. org/10.1080/00207540601159516


• Ghosh, T., Sengupta, S., Chattopadhyay, M., & Dan, P. (2011). Meta-heuristics in cellular manufacturing: A state-of-the-art review. International Journal of Industrial Engineering Computations, 2(1), 87-122.


• Hachicha, W., Masmoudi, F., & Haddar, M. (2007). An improvement of a cellular manufacturing system design using simulation analysis. Internationale Journal of Simulation Modeling, 6(4), 193-205. DOI:10.2507/IJSIMM06(4)1.089


• Haraguchi, H. (2019). A Study on Operator Allocation Method Considering the Productivity and the Training Effect in Labor-Intensive Manufacturing System. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management. DOI: 10.1109/IEEM44572.2019.8978613


• Heragu, S. S. (1994). Group technology and cellular manufacturing. IEEE Transactions on Systems, Man, and Cybernetics, 24(2), 203-215. DOI: 10.1109/21.281420


• Khojasteh, G., Daei Karimzadeh, S., Sharifi Ranani, H. (2019). Credit Risk Measurement of Trusted Customers Using Logistic Regression and Neural Networks. Journal of System Management, 5(3), 91-104. 20.1001.1.23222301.2019.5.3.6.1


• Martin, E., Gomez-Aguilar, F., Solis-Perez, E. (2022). Artificial neural networks: a practical review of applications involving fractional calculus. The European Physical Journal, (231), 2059–2095. doi.org/10.1140/epjs/s11734-022-00455-3


• 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. org/10.1016/j.amc.2007.01.060


• Mak, K. L., Peng, P., Wang, X. X., & Lau, T. L. (2014). An ant colony optimization algorithm for scheduling virtual cellular manufacturing systems. International Journal of Computer Integrated Manufacturing, 20(6), 524-537. org/10.1080/09511920600596821


• 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:10.1504/EJIE.2008.017688


• Moscote, D., Tyagunov, M. (2022). Electricity consumption forecasting using neural networks for low-carbon power systems planning. Journal of Business Management, 14(2), 1-16. doi.org/10.1051/e3sconf/202235101069


• Murugan, M., & Selladurai, V. (2013). Optimization and implementation of cellular manufacturing system in a pump industry using three cell formation algorithms. The International Journal of Advanced Manufacturing Technology, 35(1), 135-149. DOI:10.1007/s00170-006-0710-4


• Quintanar, R., Galvan, C., Tejada, L., Tejada, J. (2022). Narx Neural Networks Models for Prediction of Standardized Precipitation Index in Central Mexico. Journal of Atmosphere, 13(6), 117-130. https://doi.org/10.3390/


• Safaei, N., Saidi-Mehrabad, M., & Jabal-Ameli, M. (2008). A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. European Journal of Operational Research, 185(2), 563-592. org/10.1016/j.ejor.2006.12.058


• Salari, A., Vakilifard, H., Talebnia, G. (2019). The Comparison of Applying a Designed Model to Measure Credit Risk between Melli and Mellat Banks. Journal of System Management, 5(4), 149-160. 1001.1.23222301.2019.5.4.13.0


• Sherej-Sharifi, A., Bazaiee, G. (2018). Neural Network Approach for Herbal Medicine Market Segmentation. Journal of System Management, 4(3), 35-58.


• Sun, L., Liang, F., Cui, W. (2021). Artificial Neural Network and Its Application Research Progress in Chemical Process. Journal of Electrical Engineering and Systems Science, 18(4), 263-277. doi.org/10.48550/arXiv.2110.09021


• 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. org/10.1016/j.amc.2006.05.172


• Webber, R., Xu, H., Chang, R., Liu, S. (2022). Application of Artificial Neural Networks in Construction Management: A Scientometric Review. International Journal of Building, 12(7), 27-40. doi.org/10.3390/buildings12070952


• Wu, L., Li, L., Tan, L., Niu, B., Wang, R., & Feng, Y. (2020). Improved similarity coefficient and clustering algorithm for cell formation in cellular manufacturing systems. Engineering Optimization, 52(11), 1923-1939. org/10.1080/0305215X.2019.1692204


• Wu, X., Chu, C. H., Wang, Y., & Yan, W. (2007). A genetic algorithm for cellular manufacturing design and layout. European journal of operational research, 181(1), 156-167. org/10.1016/j.ejor.2006.05.035


• 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. org/10.1080/00207540500338252


• Wemmerlov, U., & Hyer, N. L. (1989). Cellular manufacturing in the US industry: a survey of users. The international journal of production research, 27(9), 1511-1530. org/10.1080/00207548908942637


• Yari, A., Keramati, M., Etemadi, A., Kouloubandi, A. (2021). Design and Implementation of Organizational Architecture in Organizations in Charge of Combating Smuggling of Goods and Currency with the Aim of Improving the Management of Organizational Networks. Journal of System Management, 7(1), 121-153. doi: 10.30495/jsm.2021.1923975.1443