Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

References

• Antonelli, M., P. Ducange, B. Lazzerini, and F. Marcelloni. 2016. “Multi-objective Evolutionary Design of Granular Rule-Based Classifiers.” Granular Computing 1 (1): 37–58. doi: 10.1007/s41066-015-0004-z
   Google Scholar
• Brailovsky, V. L., O. Barzilay, and R. Shahave. 1999. “On Global, Local, Mixed and Neighborhood Kernels for Support Vector Machines.” Pattern Recognition Letters 20 (11): 1183–1190. doi: 10.1016/S0167-8655(99)00086-0
   Web of Science ®Google Scholar
• Caniato, F., M. Kalchschmidt, S. Ronchi, R. Verganti, and G. Zotteri. 2005. “Clustering Customers to Forecast Demand.” Production Planning and Control 16 (1): 32–43. doi: 10.1080/09537280512331325155
   Web of Science ®Google Scholar
• Chang, P. C., Y. W. Wang, and C. Y. Tsai. 2005. “Evolving Neural Network for Printed Circuit Board Sales Forecasting.” Expert Systems with Applications 29 (1): 83–92. doi: 10.1016/j.eswa.2005.01.012
   Web of Science ®Google Scholar
• Corberán-Vallet, A., J. D. Bermúdez, and E. Vercher. 2011. “Forecasting Correlated Time Series with Exponential Smoothing Models.” International Journal of Forecasting 27 (2): 252–265. doi: 10.1016/j.ijforecast.2010.06.003
   Web of Science ®Google Scholar
• Cortes, C., and V. Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–297.
   Web of Science ®Google Scholar
• Cui, J., S.-F. Liu, B. Zeng, and N.-M. Xie. 2013. “A Novel Grey Forecasting Model and Its Optimization.” Applied Mathematical Modelling 37 (6): 4399–4406. doi: 10.1016/j.apm.2012.09.052
   Web of Science ®Google Scholar
• Dean, P. R., D. Xue, and Y. L. Tu. 2009. “Prediction of Manufacturing Resource Requirements from Customer Demands in Mass-Customisation Production.” International Journal of Production Research 47 (5): 1245–1268. doi: 10.1080/00207540701557197
   Web of Science ®Google Scholar
• Gao, J., Y. Yao, V. C. Y. Zhu, L. Sun, and L. Lin. 2011. “Service-Oriented Manufacturing: A New Product Pattern and Manufacturing Paradigm.” Journal of Intelligent Manufacturing 22 (3): 435–446. doi: 10.1007/s10845-009-0301-y
   Web of Science ®Google Scholar
• Gebauer, H., E. Fleisch, and T. Friedli. 2005. “Overcoming the Service Paradox in Manufacturing Companies.” European Management Journal 23 (1): 14–26. doi: 10.1016/j.emj.2004.12.006
   Google Scholar
• Hsu, L., and C. Wang. 2009. “Forecasting Integrated Circuit Output Using Multivariate Grey Model and Grey Relational Analysis.” Expert Systems with Applications 36 (2): 1403–1409. doi: 10.1016/j.eswa.2007.11.015
   Web of Science ®Google Scholar
• Huang, M. G., P. L. Chang, and Y. C. Chou. 2008. “Demand Forecasting and Smoothing Capacity Planning for Products with High Random Demand Volatility.” International Journal of Production Research 46 (12): 3223–3239. doi: 10.1080/00207540601094457
   Web of Science ®Google Scholar
• Huang, S., S. Zeng, Y. Fan, and G. Q. Huang. 2011. “Optimal Service Selection and Composition for Service-Oriented Manufacturing Network.” International Journal of Computer Integrated Manufacturing 24 (5): 416–430. doi: 10.1080/0951192X.2010.511657
   Web of Science ®Google Scholar
• Kück, M., B. Scholz-Reiter, and M. Freitag. 2014. “Robust Methods for the Prediction of Customer Demands Based on Nonlinear Dynamical Systems.” Procedia CIRP 19: 93–98. doi: 10.1016/j.procir.2014.05.014
   Google Scholar
• Leandro, M., B. Rosangela, and G. Fernando. 2016. “Evolving Granular Analytics for Interval Time Series Forecasting.” Granular Computing 1 (4): 213–224. doi: 10.1007/s41066-016-0016-3
   Google Scholar
• Levis, A., and L. Papageorgiou. 2005. “Customer Demand Forecasting via Support Vector Regression Analysis.” Chemical Engineering Research and Design 83 (8): 1009–1018. doi: 10.1205/cherd.04246
   Web of Science ®Google Scholar
• Li, T., L. He, and H. Li. 2008. “Prediction and Analysis of Chaotic Time Series on the Basis of Support Vector.” Journal of Systems Engineering and Electronics 19 (4): 806–811. doi: 10.1016/S1004-4132(08)60157-7
   Web of Science ®Google Scholar
• Li, D., C. Yeh, and C. Chang. 2009. “An Improved Grey-Based Approach for Early Manufacturing Data Forecasting.” Computers & Industrial Engineering 57 (4): 1161–1167. doi: 10.1016/j.cie.2009.05.005
   Web of Science ®Google Scholar
• Lin, S. W., Z. J. Lee, S. C. Chen, and T. Y. Tseng. 2008. “Parameter Determination of Support Vector Machine and Feature Selection Using Simulated Annealing Approach.” Applied Soft Computing 8 (4): 1505–1512. doi: 10.1016/j.asoc.2007.10.012
   Web of Science ®Google Scholar
• Lingras, P., F. Haider, and M. Triff. 2016. “Granular Meta-Clustering Based on Hierarchical, Network, and Temporal Connections.” Granular Computing 1 (1): 71–92. doi: 10.1007/s41066-015-0007-9
   Google Scholar
• Livi, L., and A. Sadeghian. 2016. “Granular Computing, Computational Intelligence, and the Analysis of Non-geometric Input Spaces.” Granular Computing 1 (1): 13–20. doi: 10.1007/s41066-015-0003-0
   Google Scholar
• Miao, X., and B. Xi. 2008. “Agile Forecasting of Dynamic Logistics Demand.” Transport 23 (1): 26–30. doi: 10.3846/1648-4142.2008.23.26-30
   Web of Science ®Google Scholar
• Nagorny, K., A. W. Colombo, and U. Schmidtmann. 2012. “A Service- and Multi-agent-Oriented Manufacturing Automation Architecture.” Computers in Industry 63 (8): 813–823. doi: 10.1016/j.compind.2012.08.003
   Web of Science ®Google Scholar
• Nejadgholi, I., M. H. Moradi, and F. Abdolali. 2011. “Using Phase Space Reconstruction for Patient Independent Heartbeat Classification in Comparison with Some Benchmark Methods.” Computers in Biology and Medicine 41 (6): 411–419. doi: 10.1016/j.compbiomed.2011.04.003
   PubMed Web of Science ®Google Scholar
• Papadimitriou, T., P. Gogas, and E. Stathakis. 2014. “Forecasting Energy Markets Using Support Vector Machines.” Energy Economics 44: 135–142. doi: 10.1016/j.eneco.2014.03.017
   Web of Science ®Google Scholar
• Peters, G., and R. Weber. 2016. “DCC: A Framework for Dynamic Granular Clustering.” Granular Computing 1 (1): 1–11. doi: 10.1007/s41066-015-0012-z
   Google Scholar
• Qin, Y. Q., W. D. Cai, and B. R. Yang. 2008. “Research on Phase Space Reconstruction of Non-linear Time Series.” Journal of System Simulation 20 (11): 2969–2973.
   Google Scholar
• Radosavljević, J., M. Jevtić, and D. Klimenta. 2016. “Energy and Operation Management of a Microgrid Using Particle Swarm Optimization.” Engineering Optimization 48 (5): 811–830. doi: 10.1080/0305215X.2015.1057135
   Web of Science ®Google Scholar
• Roy, R., T. S. Baines, H. W. Lightfoot, O. Benedettini, and J. M. Kay. 2009. “The Servitization of Manufacturing.” Journal of Manufacturing Technology Management 20 (5): 547–567. doi: 10.1108/jmtm.2009.06820eaa.001
   Google Scholar
• Scholz-Reiter, B., M. Kück, and D. Lappe. 2014. “Prediction of Customer Demands for Production Planning—Automated Selection and Configuration of Suitable Prediction Methods.” CIRP Annals—Manufacturing Technology 63 (1): 417–420. doi: 10.1016/j.cirp.2014.03.106
   Web of Science ®Google Scholar
• Sivakumar, B. 2002. “A Phase-Space Reconstruction Approach to Prediction of Suspended Sediment Concentration in Rivers.” Journal of Hydrology 258 (1): 149–162.
   Web of Science ®Google Scholar
• Smits, G. F., and E. M. Jordan. 2002. “Improved SVM Regression Using Mixtures of Kernels.” In Proceedings of the International Joint Conference on Neural Networks. Honolulu: Institute of Electrical and Electronics Engineers Inc. 3: 2785–2790.
   Google Scholar
• Sujay, R. N., and P. C. Deka. 2014. “Support Vector Machine Applications in the Field of Hydrology: A Review.” Applied Soft Computing 19: 372–386. doi: 10.1016/j.asoc.2014.02.002
   Web of Science ®Google Scholar
• Suykens, J. A. K., and J. Vandewalle. 1999. “Least Squares Support Vector Machine Classifiers.” Neural Processing Letters 9 (3): 293–300. doi: 10.1023/A:1018628609742
   Web of Science ®Google Scholar
• Takens, F. 1981. “Determining Strange Attractors in Turbulence.” Lecture Notes in Mathematics 898: 361–381.
   Google Scholar
• Valipour, M., M. E. Banihabib, and S. M. R. Behbahani. 2013. “Comparison of the ARMA, ARIMA, and the Autoregressive Artificial Neural Network Models in Forecasting the Monthly Inflow of Dez Dam Reservoir.” Journal of Hydrology 476 (2): 433–441. doi: 10.1016/j.jhydrol.2012.11.017
   Web of Science ®Google Scholar
• Voyant, C., M. Muselli, C. Paoli, and N. Marie-Laure. 2011. “Optimization of an Artificial Neural Network Dedicated to the Multivariate Forecasting of Daily Global Radiation.” Energy 36 (1): 348–359. doi: 10.1016/j.energy.2010.10.032
   Web of Science ®Google Scholar
• Wang, C.-C. 2011. “A Comparison Study Between Fuzzy Time Series Model and ARIMA Model for Forecasting Taiwan Export.” Expert Systems with Applications 38 (8): 9296–9304. doi: 10.1016/j.eswa.2011.01.015
   Web of Science ®Google Scholar
• Wang, T., D. Zhao, and S. Tian. 2015. “An Overview of Kernel Alignment and Its Applications.” Artificial Intelligence Review 43 (2): 179–192. doi: 10.1007/s10462-012-9369-4
   Web of Science ®Google Scholar
• Wu, H. C. 2007. “The Karush-Kuhn-Tucker Optimality Conditions in an Optimization Problem with Interval-Valued Objective Function.” European Journal of Operational Research 176 (1): 46–59. doi: 10.1016/j.ejor.2005.09.007
   Web of Science ®Google Scholar
• Wu, Q. 2010. “Product Demand Forecasts Using Wavelet Kernel Support Vector Machine and Particle Swarm Optimization in Manufacture System.” Journal of Computational and Applied Mathematics 233 (10): 2481–2491. doi: 10.1016/j.cam.2009.10.030
   Web of Science ®Google Scholar
• Zheng, Z., X. Chen, C. Liu, and K. Huang. 2016. “Using Support Vector Machine and Dynamic Parameter Encoding to Enhance Global Optimization.” Engineering Optimization 48 (5): 851–867. doi: 10.1080/0305215X.2015.1057056
   Web of Science ®Google Scholar
• Zhu, B., and Y. Wei. 2013. “Carbon Price Forecasting with a Novel Hybrid ARIMA and Least Squares Support Vector Machines Methodology.” Omega 41 (3): 517–524. doi: 10.1016/j.omega.2012.06.005
   Web of Science ®Google Scholar
• Zupančič, R., A. Sluga, and P. Butala. 2012. “A Service Network for the Support of Manufacturing Operations.” International Journal of Computer Integrated Manufacturing 25 (9): 790–803. doi: 10.1080/0951192X.2012.665188
   Web of Science ®Google Scholar