Skip to main content
SpringerLink
Log in

Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs

  • Conference paper
Research and Development in Intelligent Systems XXV (SGAI 2008)

Abstract

Dynamic pricing is a pricing strategy where a firm adjust the price for their products and services as a function of its perceived demand at different times. In this paper, we show how Evolutionary algorithms (EA) can be used to analyse the effect of demand uncertainty in dynamic pricing. The experiments are conducted in a range of dynamic pricing problems considering a number of different stochastic scenarios with a number of different EAs. The results are analysed, which suggest that higher demand fluctuation may not have adverse effect to the profit in comparison to the lower demand fluctuation, and that the reliability of EA for finding accurate policy could be higher when there is higher fluctuation then when there is lower fluctuation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, W., Marn, M.V., Zawada, C: Price smarter on the net. Harvard Business Review 79 2001)

    Google Scholar 

  2. Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning,. Tech. Rep. CMU-CS-94-163, Pittsburgh, PA (1994). URL citeseer.nj.nec.com/baluja94population.html

    Google Scholar 

  3. Bichler, M., Kalagnanam, J., Katircioglu, K., King, A.J., Lawrence, R.D., Lee, H.S., Lin, G.Y., Lu., Y.: Applications of flexible pricing in business-to-business electronic commerce. IBM Systems Journal 41(2), 287–302 (2002)

    Article  Google Scholar 

  4. de Bonet, J.S., Isbell Jr., C.L., Viola, P.: MIMIC: Finding optima by estimating probability densities. In: M.C. Mozer, M.I. Jordan, T. Petsche (eds.) Advances in Neural Information Processing Systems, vol. 9. The MIT Press (1997). URL citeseer.nj.nec.com/debonet96mimic.html

    Google Scholar 

  5. Etxeberria, R., Larrañaga, P.: Global optimization using Bayesian networks. In: A. Ochoa, M.R. Soto, R. Santana (eds.) Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99), pp. 151–173. Havana, Cuba (1999)

    Google Scholar 

  6. Ferdows, K., Lewis, M.A., Machura, J.A.M.: Rapid-fire fulfilment. Harvard Business Review 82, 104–110 (2004)

    Google Scholar 

  7. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989)

    Google Scholar 

  8. Inza, I., Merino, M., Larrañaga, P., Quiroga, J., Sierra, B., Girala, M.: Feature subset selection by population-based incremental learning. A case study in the survival of cirrhotic patients with TIPS. Artificial Intelligence in Medicine (2001)

    Google Scholar 

  9. Kirkpatrick, S., Gelatt, CD., Vecchi, M.P; Optimization by simulated annealing. Science, Number 4598, 13 May 1983 220, 4598, 671–680 (1983).URL citeseer.ist.psu.edu/kirkpatrick83optimization.html

    Article  MathSciNet  Google Scholar 

  10. Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers (2002)

    Google Scholar 

  11. McWilliams, G.: Lean machine: How dell fine-tunes itspc pricing to gain edge in slow market. Wall Street Journal (June 8, 2001)

    Google Scholar 

  12. Mitchell, M.: An Introduction To Genetic Algorithms. MIT Press, Cambridge, Massachusetts (1997)

    Google Scholar 

  13. Mühlenbein, H., Mahnig, T.: FDA — A scalable evolutionary algorithm for the optimization of additively decomposed functions. Evolutionary Computation 7(4), 353–376 (1999). URL citeseer.nj.nec.com/uhlenbein99fda.html

    Article  Google Scholar 

  14. Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions: I. binary parameters. In: H.M. Voigt, W. Ebeling, I. Rechenberg, H.P. Schwefel (eds.) Parallel Problem Solving from Nature — PPSN IV, pp. 178–187. Springer, Berlin (1996). URL citeseer.nj.nec.com/uehlenbein96from.html

    Chapter  Google Scholar 

  15. Narahari, Y., Raju, C.V., Ravikumar, K., Shah, S.: Dynamic pricing models for electronic business. Sadhana 30(part 2,3), 231–256 (April/June 2005)

    Article  MATH  MathSciNet  Google Scholar 

  16. Netessine, S., Shumsky, R.: Introduction to the theory and practice of yield management. INFORMS Transactions on Education 3(1) (2002)

    Google Scholar 

  17. Owusu, G., Dorne, R., Voudouris, C, Lesaint, D.: Dynamic planner: A decision support tool for resource planning, applications and innovations in intelligent systems x. In: Proceedings of ES 2002, pp. 19–31 (2002)

    Google Scholar 

  18. Owusu, G., Voudouris, C, Kern, M., Garyfalos, A., Anim-Ansah, G., Virginas, B.: On Optimising Resource Planning in BT with FOS. In: Proceedings International Conference on Service Systems and Service Management (2006)

    Google Scholar 

  19. Parsopoulos, K., Vrahatis, M.: Particle swarm optimization method for constrained optimization problems. Intelligent Technologies—Theory and Application: New Trends in Intelligent Technologies, volume 76 of Frontiers in Artificial Intelligence and Applications pp. 214–220 (2002)

    Google Scholar 

  20. Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian Optimization Algorithm. In: W. Banzhaf et al. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference GECC099, vol. I, pp. 525–532. Morgan Kaufmann Publishers, San Fransisco, CA (1999)

    Google Scholar 

  21. Pelikan, M., Mühlenbein, H.: The bivariate marginal distribution algorithm. In: R. Roy, T. Furuhashi, P.K. Chawdhry (eds.) Advances in Soft Computing — Engineering Design and Manu-facturing, pp. 521–535. Springer-Verlag, London (1999)

    Google Scholar 

  22. Petrovski, A., Shakya, S., McCall, J.: Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms. In: proceedings of Genetic and Evolutionary Computation Conference (GECCO 2006). ACM, Seattle, USA (2006)

    Google Scholar 

  23. Phillips, R.: Pricing and revenue optimization. Stanford University Press (2005)

    Google Scholar 

  24. Sahay, A.: How to reap higher profits with dynamic pricing. MIT Sloan management review 48, 53–60 (2007)

    Google Scholar 

  25. Shakya, S.: Deum: A framework for an estimation of distribution algorithm based on markov random fields. Ph.D. thesis, The Robert Gordon University, Aberdeen, UK (April 2006)

    Google Scholar 

  26. Shakya, S., Oliveira, F., Owusu, G.: An Application of EDA and GA to Dynamic Pricing. In: proceedings of Genetic and Evolutionary Computation Conference (GECCO2007), pp. 585–592. ACM, London, UK (2007)

    Chapter  Google Scholar 

  27. Talluri, K., van Ryzin, G.: The Theory and Practice of Revenue Management. Springer, Berlin Heidelberg, New York (2004)

    MATH  Google Scholar 

  28. Voudouris, C, Owusu, G., Dorne, R., Ladde, C, Virginas, B.: Arms: An automated resource management system for british telecommunications pic. European Journal for Operational Research 171, 951–961 (2006)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Research Centre, BT Group Chief Technology Office, Adastral Park, Ipswich, IP5 3RE, UK

    Siddhartha Shakya & Gilbert Owusu

  2. Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK

    Fernando Oliveira

Authors
  1. Siddhartha Shakya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gilbert Owusu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Technology, University of Portsmouth, Portsmouth, UK

    Max Bramer BSc, PhD, CEng, CITP, FBCS, FIET, FRSA, FHEA

  2. University of Greenwich, UK

    Miltos Petridis DipEng, MBA, PhD, MBCS, AMBA

  3. Department of Computer Science, University of Liverpool, Liverpool, UK

    Frans Coenen BSc, PhD

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag London Limited

About this paper

Cite this paper

Shakya, S., Oliveira, F., Owusu, G. (2009). Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs. In: Bramer, M., Petridis, M., Coenen, F. (eds) Research and Development in Intelligent Systems XXV. SGAI 2008. Springer, London. https://doi.org/10.1007/978-1-84882-171-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-171-2_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-170-5

  • Online ISBN: 978-1-84882-171-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation