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2023 | 18 | 29-46

Article title

Multicriteria models in revenue management

Content

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Languages of publication

Abstracts

EN
Revenue management (RM) deals with selling the right product to the right customer at the right time at the right price through the right channel to maximize revenue. The innovation of RM lies in the way decisions are made. The performance of revenue management approaches can be evaluated against several criteria. Both discrete and continuous multicriteria models can be used to analyse RM. The performance pyramid is a comprehensive, fully integrated per performance system that captures multiple perspectives such as internal, financial, customer and innovation. The assessment is based on a combination of Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) approaches. Customer behaviour modelling is gaining increasing attention in revenue management. Customer choice models can be extended with more inputs and more outputs. Evaluation of alternatives can be performed using DEA based evaluation methods. The search for an efficient frontier in a DEA model can be formulated as a multiobjective linear programming problem. We propose to use an Aspiration Level Oriented Procedure (ALOP) to solve the problem.

Year

Volume

18

Pages

29-46

Physical description

Dates

published
2023

Contributors

author
  • Prague University of Economics and Business, Czech Republic
  • University of Finance and Administration, Prague, Czech Republic

References

  • Azadeh S.S., Hosseinalifam M., Savard G. (2015), The Impact of Customer Behavior Models on Revenue Management Systems, Computational Management Science, 12, 99-109.
  • Charnes A., Cooper W.W., Rhodes E. (1978), Measuring Efficiency of Decision-Making Units, European Journal of Operational Research, 2(6), 429-444.
  • Charnes A., Cooper W.W., Seiford L.M. (1994), Data Envelopment Analysis: Theory, Methodology, and Applications, Kluwer Publ., Boston.
  • Chen L., Homem-de-Mello T. (2010), Mathematical Programming Models for Revenue Management under Customer Choice, European Journal of Operational Research, 203, 294-305.
  • Cooper W.W., Seiford L.M., Tone K. (2000), Data Envelopment Analysis, Kluwer Publ., Boston.
  • Cooper W.W., Tone K. (1995), A Survey of Some Recent Developments in Data Envelopment Analysis, Proceedings of the EURO XIV Conference, Jerusalem, 149-168.
  • Fiala P. (1997), Models of Cooperative Decision Making [in:] T. Gal, G. Fandel (eds.), Multiple Criteria Decision Making, Springer, Berlin, 128-136.
  • Gallego G., Iyengar G., Phillips R., Dubey A. (2004), Managing Flexible Products on a Network, Tech. Rep. TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University.
  • Kaplan R.S., Norton D.P. (2015), Balanced Scorecard Success: The Kaplan-Norton Collection (4 Books), Harvard Business Review Press.
  • Korhonen P. (1997), Searching the Efficient Frontier in Data Envelopment Analysis, IIASA Report IR-97-79, IIASA, Laxenburg.
  • Liu Q., van Ryzin G.J. (2007), Strategic Capacity Rationing to Induce Early Purchases, Working Paper, Columbia University.
  • Rouse P., Puterill M., Ryan D. (1997), Towards a General Managerial Framework for Performance Measurement: A Comprehensive Highway Maintenance Application, Journal of Productivity Analysis, 8, 127-149.
  • van Ryzin G.J., Liu Q. (2008), On the Choice-based Linear Programming Model for Network Revenue Management, Manufacturing & Service Operations Management, 10, 288-311.
  • Saaty T.L. (1996), The Analytic Hierarchy Process, RWS Publications, Pittsburgh.
  • Saaty T.L. (2001), Decision Making with Dependence and Feedback: The Analytic Network Process, RWS Publications, Pittsburgh.
  • Shen Z.-J., Su X. (2007), Customer Behavior Modeling in Revenue Management and Auctions, Production and Operations Management, 16, 713-728.
  • Strauss A.K., Klein R., Steinhardt C. (2018), A Review of Choice-based Revenue Management: Theory and Methods, European Journal of Operational Research, 271, 375-387.
  • Talluri K., van Ryzin G.J. (2004a), Revenue Management under a General Discrete Choice Model of Consumer Behaviour, Management Science, 50, 15-33.
  • Talluri K.T., van Ryzin G.J. (2004b), The Theory and Practice of Revenue Management, Kluwer Publ., Boston.
  • Yeoman I. (2022), The Continuing Evolution of Revenue Management Science, Journal of Revenue and Pricing Management, 21, 1-2.

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
33948716

YADDA identifier

bwmeta1.element.ojs-doi-10_22367_mcdm_2023_18_02
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