Designing Cell Production Arrangement Scenarios with the Approach of Artificial Neural Networks
الموضوعات :
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
تاريخ الإرسال : 02 الأحد , محرم, 1444
تاريخ التأكيد : 19 السبت , ربيع الأول, 1444
تاريخ الإصدار : 07 الخميس , جمادى الأولى, 1444
الکلمات المفتاحية:
Scenario Analysis,
production line arrangement,
Artificial Neural Networks,
cell production,
ملخص المقالة :
The arrangement of machines and how to move them is one of the most important issues in factories and production units, which always imposes a lot of costs on the collections. Although the arrangement of machines is done once over a long period of time, its effects are very widespread. Accordingly, it is necessary to pay more attention to the matter of arrangement. Today, cellular production is also one of the widespread production methods at the industrial level, which requires this precision. The current research aims to produce new arrangements by using artificial neural networks. The way of working is that by using the data related to the number of production parts, the production time of each part, and the group of parts under investigation, as well as the costs of the devices, this clustering is done in 3 modes of 4, 6, and 9. Performing this type of clustering has higher accuracy and speed than other methods, and the results may be somewhat different in each scenario and with each clustering time, which increases flexibility in selection.
المصادر:
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
