Forecasting operation times by using Artificial Intelligence

Abstract

Due to increased competition, companies must reduce delivery and costs on time and provide the desired product characteristics. This study was carried out in a firm that manufactures napkin machines according to the order. The most important problem is that the suppliers cannot deliver to customers on time. For effective production planning, it is necessary to use the correct operation times for each machine used. The times were estimated by using the Artificial Neural Network (ANN) approach and the Taguchi Design of Experiment was used to estimate the optimal combination of ANN parameters. According to the results of the research, it is found that the number of layers and neurons have significant influence. By using the ANN method, the time spent in parameter design is effectively reduced and the efficiency of the algorithm is increased. Estimation performance is compared with the statistical analysis. This model proved to be statistically reliable in estimating operation times. Thus, the operators will be able to estimate the processing times for new designs.

Keywords

• Artificial Neural Networks,Design of Experiment,Forecasting,Metaheuristics

References

1. 1. Hamzaçebi C. Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy Policy , 2007.35:p.2009–2016.
2. 2. Catalao JPS, Mariano SJPS, Mendes VMF, Ferreira LAFM Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research, 2007. 77:p.1297-1304.
3. 3. Almeida LB, Fiesler E, Beale R Multilayer Perceptrons, Handbook of Neural Computation. 1997, Oxford University Press.
4. 4. Taeho Jo The effect of mid-term estimation on back propagation for time series prediction. Neural Computing and Applications, 2010. 19:p.1237-1250.
5. 5. Graupe D, Principles of Artificial Neural Networks (2ndEdition), 2007,World Scientific.
6. 6. Zapranis A, Alexandridis A, Modeling and forecasting cumulative average temperature and heating degree day indices for weather derivative pricing, Neural Comput & Applic , 2011.20:p.87-801.
7. 7. Robert F, Chevalier RF, Hoogenboom G, McClendon RW, Paz JA, Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural. Neural Computing & Applications,2011. 20:p.151-159.
8. 8. Wu J, Long J, Liu M., Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm, Neurocomputing, 2015. 148: p.136-142.

9. 9. Döşoğlu M.K, Arsoy B., Modeling and simulation of static loads for wind power applications. Neural Computing and Applications, 2014. 25(5): p.997-1006.
10. 10. Kim KJ and Ahn H., Simultaneous optimization of artificial neural networks for financial forecasting. Applied Intelligence, 2012. 36 (4), p. 887-898.
11. 11. Pan W., The use of genetic programming for the construction of a financial management model in an enterprise. Applied Intelligence, 2012. 36(2), p 271-279.
12. 12. Zhong H., Miao C., Shen Z., Feng Y. ,Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings, Neurocomputing, 2014. 128: p.285-295.
13. 13. Yu L, Lai KK, Wang S., Multistage RBF neural network ensemble learning for exchange rates forecasting, Neurocomputing, 2008. 71:p.3295– 3302.
14. 14. Rehman M, Khan GM, Mahmud A., Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network, IERI Procedia, 2014. 10:p.239-24.
15. 15. Roy SS and Pratihar DK., Soft computing-based approaches to predict energy consumption and stability margin of six-legged robots moving on gradient terrains. Applied Intelligence, 2012. 37(1): p. 31-46.
16. 16. Xiong G, Cheng J, Wu X, Chen YL, Ou Y, Xu Y, An energy model approach to people counting for abnormal crowd behavior detection. Neurocomputing, 2012.83:p.121-135
17. 17. Luque C, Valls JM and Isasi P Time series prediction evolving Voronoi regions. Applied Intelligence, 2011. 34(1), pp 116-126
18. 18. Nekoukar V and Beheshti MTH A local linear radial basis function neural network for financial time-series forecasting. Applied Intelligence, 2010. 33(3) 352-356
19. 19. Adhikari R., A neural network based linear ensemble framework for time series forecasting. Neurocomputing, 2015. 157(1): p.231–242 . 20. Firmino P, Neto P, Ferreira T., Error modeling approach to improve time series forecasters. Neurocomputing, 2015. 153(4): p.242-254.
20. 21. Chen D., Wang J., Zou F., Yuan W., Hou W., Time series prediction with improved neuro-endocrine model, Neural Computing and Applications, 2014. 24(6): p.1465-1475.
21. 22. Chen H., Zhang G., Zhu D., Lu L., A patent time series processing component for technology intelligence by trend identification functionality, Neural Computing and applications, 2015. 26(2):345-353.
22. 23. Huang S., Chen J., Luo Z., Retraction Note to: Sparse tensor CCA for color face recognition. Neural Computing and Applications, 2014. 25(7-8):2091
23. 24. Çetişli B., Edizkan R., Use of wavelet-based two-dimensional scaling moments and structural features in cascade neuro-fuzzy classifiers for handwritten digit recognition. Neural Computing and Applications, 2015. 26(3): p.613-624.
24. 25. Ozcan B. and Fığlalı A., Artificial neural networks for the cost estimation of stamping dies,Neural Computing and Applications, 2014. 25(3): p.717-726
25. 26. Tosun N, Ozler L A., study of tool life in hot machining using artifcial neural networks and regression analysis method. 2002. 124(1-2):p. 99-104
26. 27. Kroll E, Carver B.S. Disassembly analysis through time estimation and other metrics. Robotics and Computer-Integrated Manufacturing, 1999. 15:p.191-200
27. 28. Heo E.Y. , WonKim D. , HyunKim B.,Chen F. Estimation of NC machining time using NC block distribution for sculptured surface machining, Robotics and Computer-Integrated Manufacturing ,2006. 22(5):p 437-446.
28. 29. M. Cavazzuti, Optimization Methods: From Theory to Design, Springer-Verlag Berlin Heidelberg, 2013. DOI: 10.1007/978-3-642-31187-1_2.
29. 30. Robert M. Bethea, Benjamin S. Duran Thomas l. Boullion, Statistical Methods for Engineers and Scientists Third Edition, Revised and Expanded. 2018, USA, Routledge publishing.
30. 31. Gupta S.C, Fundamentals of Statistics Paperback, seventh revised enlarged edition.2016, Himalaya Publishing House Pvt. Ltd.
31. 32. Davim J.P, Design of Experiments in Production Engineering, 2015, Springer.
32. 33. Pal1 B., Mhashilkar A., Pandey A., Nagphase B., Chandanshive V., Cost Estimation Model (CEM) of Buildings by ANN (Artificial Neural Networks), International Advanced Research Journal in Science, Engineering and Technology 2018. 5(2).
33. 34. Relicha M., Pawlewsk P., A case-based reasoning approach to cost estimation of new product development, Neurocomputing, 2018. 272 p.40-45.
34. 35. Chamzini A.Y., Zavadskas E.K, Antucheviciene J. and Bausys R., A Model for Shovel Capital Cost Estimation, Using a Hybrid Model of Multivariate Regression and Neural Networks, Symmetry 2017. 9(12).
35. 36. Karaoglan A.S and Karademir O., Flow time and product cost estimation by using an artificial neural network (ANN): A case study for transformer orders, Journal The Engineering Economist A Journal Devoted to the Problems of Capital Investment, 2017. 62(3).
36. 37. Mana M., Burlando M., Meissner C., Evaluation of Two ANN Approaches for the Wind Power Forecast in a Mountainous Site,International Journal of Renewable Energy Research-IJRER, 2017. 7(4).