Applying moving back-propagation neural network and moving fuzzy neuron network to predict the requirement of critical spare parts

Expert Systems with Applications

Volumn 37, Issue 6, 2010, Pages 4358-4367

  Chen, Fei Long ^a   Chen, Yun Chin ^a   Kuo, Jun Yuan ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b KAINAN UNIVERSITY   (Taiwan)

Author keywords

Critical spare part; Moving back propagation neural network; Moving fuzzy neuron network; Prediction

Indexed keywords

BACK PROPAGATION NEURAL NETWORKS; DEMAND VARIATIONS; FORECASTING METHODS; GREY PREDICTION; LEADTIME; MACHINE OPERATION; NEURON NETWORKS; PREDICTION ACCURACY; PREDICTION METHODS; SPARE PARTS; WAFER TESTING;

BACKPROPAGATION; COMPUTER OPERATING PROCEDURES; STRUCTURES (BUILT OBJECTS); TORSIONAL STRESS;

NEURAL NETWORKS;

EID: 77249121017     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.11.092     Document Type: Article

References (20)

1. Ansuj A.P., Camargo M.E., Radharamanan R., and Petry D.G. Sales forecasting using time series and neural networks. Computers and Industrial Engineering 31 (1996) 421-424
2. Aronis K.P., Magou I., Dekker R., and Tagaras G. Inventory control of spare parts using a Bayesian approach: A case study. European Journal of Operational Research 154 (2004) 730-739
3. Caglar D., Li C.L., and Simchi-Levi D. Two-echelon spare parts inventory system subject to a service constraint. Institute of Industrial Engineers Transactions 36 (2004) 655-666
4. Dekker R., Kleijn M.J., and Rooij P.J. A spare parts stocking policy based on equipment criticality. International Journal of Production Economics 56 (1998) 69-77
5. Deng J.L. Introduction to grey systems theory. Journal of Grey Systems 1 (1982) 1-24
6. Ghobbar A.A., and Friend C.H. Evaluation of forecasting methods for intermittent parts demand in the field of aviation: A predictive model. Computers and Operations Research 30 (2003) 2097-2114
7. Hua Z., and Zhang B. A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation 181 (2006) 1035-1048
8. Kabir, A. B. M. Z., & Al-Olayan, A. S. (1996). A stocking policy for spare part provisioning under age based preventive replacement. European Journal of Operational Research, 90, 171-181.
9. Law R. Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management 21 (2000) 331-340
10. Li S.G., and Kuo X. The inventory management system for automobile spare parts in a central warehouse. Expert Systems with Applications 34 (2008) 1144-1153
11. Lin C.T., and Yang S.Y. Forecast of the output value of Taiwan's opto-electronics industry using the grey forecasting model. Technological Forecasting and Social Change 70 (2003) 177-186
12. Philip D.W. Neural computing: Theory and practice (1989), Van Nostrand Reinhold, New York
13. Prakash G.P., Ganesh L.S., and Rajendran C. Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics 35 (1994) 293-297
14. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representation by error propagation. Parallel Distributed Processing 1 (1986) 318-362
15. Saaty T.L. The analytic hierarchy process (1980), McGraw-Hill, New York
16. Sheu D.D., and Kuo J.Y. A model for preventive maintenance operations and forecasting. Journal of Intelligent Manufacturing 17 (2006) 441-451
17. Wen K.L. Principle and application of grey forecast (2003), Chwa Inc., Taiwan
18. Yeh I.C. Modeling chaotic two-dimensional mapping with fuzzy-neuron networks. Fuzzy Sets and System 105 (1999) 421-427
19. Yeh I.C. Classification and function mapping with fuzzy-neuron networks. Journal of Science and Technology 14 (2005) 153-159
20. Zadeh L. Fuzzy sets. Inform and Control 8 (1965) 338-353