Dynamic pricing using wavelet neural network under uncertain demands

AmalNick, M.; Qorbanian, R.

Abstract: Dynamic pricing is a kind of pricing strategy in which the price of products varies based on present demand value. So far, several research works have been reported for using neural network for pricing, such as predicting demand and modeling the customer's choices. However, less work has been performed on using them for optimizing pricing policies. In this project, we try to explain the way of combining neural network and evolutionary algorithms to optimize pricing policies. We create a neural network on the basis of demand model and benefit from evolutionary algorithms for optimizing the resulted model. This has got two privileges: First, necessary flexibilities are created by using neural network to model different demand scenarios that is occurred with different products and services, and second, using evolutionary algorithms provides us with the ability of solving complicated models. Wavelet neural network has been used and the resulted pricing policy has been compared with other demand models that are widely used. The results show that the suggested model match up well under different scenarios and presents a better pricing policy than other suggested models.

How to cite this paper

        APA: AmalNick, M & Qorbanian, R. (2017). Dynamic pricing using wavelet neural network under uncertain demands. Decision Science Letters, 6(3), 251-260.
    

        Chicago/Turabian: AmalNick, M & Qorbanian, R. 2017. "Dynamic pricing using wavelet neural network under uncertain demands." Decision Science Letters 6, no. 3 (2017): 251-260.
    

        AMA: AmalNick, M & Qorbanian, R. Dynamic pricing using wavelet neural network under uncertain demands. Decision Science Letters. 2017;6(3):251-260.
    

References

Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological measurement, 26(5), R155.
Arbib, M. A. (2003). The handbook of brain theory and neural networks. MIT press.
Brooks, C. H., Fay, S., Das, R., MacKie-Mason, J. K., Kephart, J. O., & Durfee, E. H. (1999, November). Automated strategy searches in an electronic goods market: Learning and complex price schedules. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 31-40). ACM.
Burrus, C. S., Gopinath, R. A., & Guo, H. (1997). Introduction to wavelets and wavelet transforms: a primer.
Coy, P. (2000). The power of smart pricing. Business Week, 10, 160.
den Boer, A. V. (2015). Dynamic pricing and learning: historical origins, current research, and new directions. Surveys in operations research and management science, 20(1), 1-18.
Ghose, T. K., & Tran, T. T. (2009, May). Dynamic pricing in electronic commerce using neural network. In International Conference on E-Technologies (pp. 227-232). Springer Berlin Heidelberg.
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994, June). A niched Pareto genetic algorithm for multiobjective optimization. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on (pp. 82-87). Ieee.
Hruschka, H., Fettes, W., & Probst, M. (2004). An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications. European Journal of Operational Research, 159(1), 166-180.
Kong, D. (2004, September). One dynamic pricing strategy in agent economy using neural network based on online learning. In Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 98-102). IEEE Computer Society.
Liu, G., & Wang, H. (2013). An online sequential feed-forward network model for demand curve prediction. Journal of Information & Computational Science, 10(10), 3063-3069.
Mohammadi, A. (2006). Forecasting exchange rates using neural network and wavelet transform. master’s thesis, University of Tehran (In Persian).
Netessine, S., & Shumsky, R. (2002). Introduction to the theory and practice of yield management. INFORMS Transactions on Education, 3(1), 34-44.
Özer, Ö., & Phillips, R. (2012). The Oxford handbook of pricing management. Oxford University Press.
Ramezani, S., Bosman, P. A., & La Poutré, H. (2011, August). Adaptive strategies for dynamic pricing agents. In Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02 (pp. 323-328). IEEE Computer Society.
Shakya, S., Oliveira, F., & Owusu, G. (2009). Analysing the effect of demand uncertainty in dynamic pricing with eas. In Research and Development in Intelligent Systems XXV (pp. 77-90). Springer London.
Southwell, M. (2002). Beyond the POS. Business Trends, July, 2.
Talluri, K. T., & Van Ryzin, G. J. (2006). The theory and practice of revenue management (Vol. 68). Springer Science & Business Media.
Veitch, D. (2005). Wavelet Neural Networks. Produced by University of UK.
Wasserman, P. D., & Meyer-Arendt, J. R. (1990). Neural computing, theory and practice. Applied Optics, 29, 2503.
Yao, C., Gao, X., & Yu, Y. (2013). Wind speed forecasting by wavelet neural networks: a comparative study. Mathematical Problems in Engineering, 2013.

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