Mean Local Trend Error and Fuzzy-Inference-Based Multicriteria Evaluation for Supply Chain Demand Forecasting
Jingpei Dan*,**, Fuding Xie***, Fangyan Dong*, and Kaoru Hirota*
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Department of Computer Science, Chongqing University, Chongqing 400044, P. R. China
***Department of Computer Science, Liaoning Normal University, Liaoning Dalian 116029, P. R. China
-  C. Carlsson and R. Fullér, “Fuzzy approach to the bullwhip effect,” Proc. of the 15th European Meeting on Cybernetics and Systems Research, Vienna, April 25-18, pp. 228-233, 2000.
-  B. Jeong et al., “A computerized causal forecasting system using genetic algorithms in supply chain management,” J. of Systems and software, Vol.60, No.3, pp. 223-237, 2002.
-  W. Y. Liang and C. C. Huang, “Agent-based demand forecast in multi-echelon supply chain,” Decision Support Systems, Vol.42, No.1, pp. 390-407, 2006.
-  T. Hosoda and S. M. Disney, “On variance amplification in a threeechelon supply chain with minimum mean square error forecasting,” Omega, Vol.34, No.4, pp. 344-358, 2006.
-  L. Aburto and R. Weber, “Improved supply chain management based on hybrid demand forecasts,” Applied Soft Computing, Vol.7, No.1, pp. 136-144, 2007.
-  R. Carbonneau et al., “Application of machine learning techniques for supply Chain demand forecasting,” European J. of Operational Research, Vol.184, No.3, pp. 1140-1154, 2008.
-  L. Ferbar et al., “Demand forecasting methods in a supply chain: Smoothing and denoising,” Int. J. of Production Economics, Vol.118, No.1, pp. 49-54, 2009.
-  J. P. Dan et al., “Multistep- ahead supply chain demand forecast based on echo state networks,” Proc. of the Eighth Int. Conf. on Information Management and Sciences, pp. 669-672, 2009.
-  H. E. Sayed et al., “A hybrid statistical genetic-based demand forecasting expert system,” Expert Systems with Applications, Vol.36, No.9, pp. 11662-11670, 2009.
-  Q. Wu, “Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system,” J. of Computational and Applied Mathematics, Vol.233, No.10, pp. 2481-2491, 2010.
-  H. L. Lee et al., “Information distortion in a supply chain: the bullwhip effect,” Management Science, Vol.43, No.4, pp. 546-558, 1997.
-  R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. of Forecasting, Vol.22, No.4, pp. 679-688, 2006.
-  A. Kerkkänen et al., “Demand forecasting errors in industrial context: Measurement and impacts,” Int. J. of Production Economics, Vol.118, No.1, pp. 43-48, 2009.
-  R. Grimberg et al., “Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors,” Sensors and Actuators A: Physical, Vol.81, No.1-3, pp. 248-250, 2000.
-  H. C. Chou et al., “Evaluating new drugs by fuzzy inference system,” Int. Computer Symposium, Taipei, Taiwan, 2004.
-  W. Ocampo-Duque et al., “Assessing water quality in rivers with fuzzy inference systems: A case study,” Environment International, Vol.32, No.6, pp. 733-742, 2006.
-  S. M. Mazloumzadeh et al., “Evaluation of general-purpose lifters for the date harvest industry based on a fuzzy inference system,” Computers and Electronics in Agriculture, Vol.60, No.1, pp. 60-66, 2008.
-  F. Ahmed et al., “Fuzzy inference system for software product family process evaluation,” Information Sciences, Vol.178, No.13, pp. 2780-2793, 2008.
-  J. Chen et al., “Study a Fuzzy Inference System on the Supplier Evaluation in E-Manufacturing,” Applied Mechanics and Materials, Vol.16, No.19, pp. 189-192, 2009.
-  T. J. Ross, “Fuzzy logic with engineering applications,” John Wiley & Sons, Second edition, 2004.
-  V. Kreinovich et al., “Gaussian membership functions are most adequate in representing uncertainty in measurements,” NASA, Johnson Space Center, North American Fuzzy Logic Information Processing Society (NAFIPS 1992), Vol.2, pp. 618-624, 1992.
-  StatsCan, Statistics Canada Table 304-0014, 2004.
-  Z. Chen and Y. Yang, “Assessing Forecast Accuracy Measures,” 2004.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.