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Year 2020, , 62 - 67, 30.06.2020

Asli Emine Ertugrul Ramazan Sahın

https://doi.org/10.17261/Pressacademia.2020.1214

Abstract

References

  • Aslani, S., Modarres, M., & Sibdari, S. (2014). On the fairness of airlines' ticket pricing as a result of revenue management techniques. Journal of Air Transport Management, 56-64. DOI: 10.1016/j.jairtraman.2014.05.004
  • Banciu, M., Odegaard, F., & Stanciu, A. (2019). Distribution-free bounds for the expected marginal seat revenue heuristic with dependent demands. Journal of Revenue and Pricing Management, 155-163. DOI: 10.1057/s41272-018-00170-6
  • Belobaba, P. (2015). Airline Revenue Management. P. Belobaba, A. Odoni, & C. Barnhart, The Global Airline Industry. Wiley.
  • Belobaba, P. (1987). Air Travel Demand and Airline Seat Inventory Management. Doctor of Philosphy. United States of America: Massachusetts Institute of Technology.
  • Belobaba, P. (1992). Optimal vs. Heuricstic Methods for Nested Seat Allocation. Proceedings of AGIFORS Reservations and Yield Management Study Group (s. 28-53). Brussels: AGIFORS.
  • Belobaba, P. (2011). Did LCCs save airline revenue management. Journal of Revenue and Pricing Management, 19-22. DOI: doi.org/10.1057/rpm.2010.45
  • Boyd, E. A., & Kallasen, R. (2004). Practice Papers: The science of revenue management when passengers purchase the lowest available fare. Journal of Revenue and Pricing Management, 171-177. DOI: 10.1057/palgrave.rpm.5170104
  • Chapuis, J. M. (2008). Basics of Dynamic Programming for Revenue Management. Revenue Yield Management eJournal, 21.
  • El-Haber, S., & El-Taha, M. (2004). Dynamic two-leg airline seat inventory control with overbooking, cancellations and no-shows. Journal of Revenue and Pricing Management, 143-170. DOI: 10.1057/palgrave.rpm.5170103
  • Elmaghraby, W., & Keskinocak, P. (2003). Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions. Management Science, 1287-1309. DOI: 10.1287/mnsc.49.10.1287.17315
  • Ertuğrul, A. E., & Şahin, R. (2019). Total Revenue Boundaries for Determining Booking Limits in Airline Revenue Management. 5th International Conference on Engineering Sciences. Ankara.
  • Fiig, T., Isler, K., Hopperstad, C., & Belobaba, P. (2010). Optimization of mixed fare structures: Theory and applications. Journal of Revenue and Pricing Management, 152-170. DOI: 10.1057/rpm.2009.18
  • Frank, M., & Friedemann, M. (2009). A simulation study: comparing the Markov decision process approach to the expected marginal seat revenue b heuristic under differing demand behaviour. International Journal of Revenue Management, 133-147. DOI: 10.1504/IJRM.2009.024161
  • Gelirli, N. (2019). Analysis of Financial Implications Due to Absence of Indegenous Aircraft Leasing Enterprice in Turkey. Journal of Business, Economics and Finance, 235-246. DOI: 10.17261/Pressacademia.2019.1167
  • Hosseinalifam, M., Marcotte, P., & Savard, G. (2016). A new bid price approach to dynamic resource allocation in network revenue management. European Journal of Operational Research, 142-150. DOI: 10.1016/j.ejor.2016.04.057
  • Huanga, K., & Liang, Y.-T. (2011). A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem. Transportation Research Part E: Logistics and Transportation Review, 333-341. DOI: 10.1016/j.tre.2010.11.005
  • Kramer, A., Friesen, M., & Shelton, T. (2018). Are airline passengers ready for personalized dynamic pricing? Journal of Revenue and Pricing Management, 115-120. DOI: 10.1057/s41272-017-0122-0
  • Lardeux, B., Sabatier, G., Delahaye, T., & Boudia, M. (2019). Yield optimization for airlines from ticket resell. Journal of Revenue and Pricing Management, 213-227. DOI: 10.1057/s41272-018-00167-1
  • Lee, T. C., & Hersh, M. (1993). A Model for Dynamic Airline Seat Inventory Control with Multiple Seat Bookings. Transportation Science, 252-265. DOI: 10.1287/trsc.27.3.252
  • Littlewood, K. (1972). Forecasting and Control of Passenger Bookings. AGIFORS Symposium Proc. (s. 95-117). Nathanya: AGIFORS Symposium Proc.
  • Phillips, R. L. (2005). Pricing and Revenue Optimization. Stanford, California: Stanford University Press.
  • Talluri, K., & van Ryzin, G. (2004). The Theory and Practice of Revenue Management. Boston: Kluwer Academic Publishers.
  • Tavana, H., & Weatherford, L. (2017). Application of an alternative expected marginal seat revenue method. Journal of Air Transport Management, 65-77. DOI: 10.1016/j.jairtraman.2017.02.006
  • Topaloğlu, H. (2008). A Stochastic Approximation Method to Compute Bid Prices in Network Revenue Management Problems. INFORMS Journal on Computing, 596-610. DOI: 10.1287/ijoc.1080.0269
  • Topaloğlu, H. (2009). Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management. Operations Research, 637-649. DOI: 10.1287/opre.1080.0597
  • Weatherford, L. (2004). EMSR versus EMSU: Revenue or utility? Journal of Revenue and Pricing Management, 277-284. DOI: 10.1057/palgrave.rpm.5170114
  • Wright, C. P., Groenevelt, H., & Shumsky, R. A. (2010). Dynamic Revenue Management in Airline Alliances. Transportation Science, 15-37. DOI: 10.1287/trsc.1090.0300
  • Yazdi, A. K., Kaviani, M. A., Hanne, T., & Ramos, A. (2020). A binary differential evolution algorithm for airline revenue management: a case study. Soft Computing. DOI: 10.1007/s00500-020-04790-2
  • Zhang, D., & Cooper, W. L. (2005). Dan Zhang, William L. Cooper. Operations Research, 415-431. DOI: 10.1287/opre.1050.0194

A NEW APPROACH FOR AIRLINE REVENUE MANAGEMENT: TOTAL REVENUE BOUNDARIES

Year 2020, , 62 - 67, 30.06.2020

Asli Emine Ertugrul Ramazan Sahın

https://doi.org/10.17261/Pressacademia.2020.1214

Abstract

Purpose - The purpose of the paper is developing and testing an advanced version of an existing method in the literature, which is used for airline revenue management (ARM).
Methodology – Expected marginal seat revenue (EMSR) is the mostly used heuristic revenue management model for literature and real life problems. In the paper, EMSR is developed, and an advanced heuristic method is formed. The new method is called total revenue boundaries (TRB). The method is tested by a problem and compared with EMSRa, EMSRb and EMSRc, which are three types of EMSR in the literature.
Findings- According to the results, TRB outperforms than EMSRa, EMSRb and EMSRc. It gives higher revenue levels with higher load factors.
Conclusion- At the end of the study, the most common ARM method is improved. By this way, a new heuristic model is gained, which does not need complicated calculations. TRB keeps the uncomplicated nature of EMSR but gives better results.

Keywords

Airline revenue management total revenue boundary expected marginal seat revenue (EMSR) optimization seat inventory management

References

  • Aslani, S., Modarres, M., & Sibdari, S. (2014). On the fairness of airlines' ticket pricing as a result of revenue management techniques. Journal of Air Transport Management, 56-64. DOI: 10.1016/j.jairtraman.2014.05.004
  • Banciu, M., Odegaard, F., & Stanciu, A. (2019). Distribution-free bounds for the expected marginal seat revenue heuristic with dependent demands. Journal of Revenue and Pricing Management, 155-163. DOI: 10.1057/s41272-018-00170-6
  • Belobaba, P. (2015). Airline Revenue Management. P. Belobaba, A. Odoni, & C. Barnhart, The Global Airline Industry. Wiley.
  • Belobaba, P. (1987). Air Travel Demand and Airline Seat Inventory Management. Doctor of Philosphy. United States of America: Massachusetts Institute of Technology.
  • Belobaba, P. (1992). Optimal vs. Heuricstic Methods for Nested Seat Allocation. Proceedings of AGIFORS Reservations and Yield Management Study Group (s. 28-53). Brussels: AGIFORS.
  • Belobaba, P. (2011). Did LCCs save airline revenue management. Journal of Revenue and Pricing Management, 19-22. DOI: doi.org/10.1057/rpm.2010.45
  • Boyd, E. A., & Kallasen, R. (2004). Practice Papers: The science of revenue management when passengers purchase the lowest available fare. Journal of Revenue and Pricing Management, 171-177. DOI: 10.1057/palgrave.rpm.5170104
  • Chapuis, J. M. (2008). Basics of Dynamic Programming for Revenue Management. Revenue Yield Management eJournal, 21.
  • El-Haber, S., & El-Taha, M. (2004). Dynamic two-leg airline seat inventory control with overbooking, cancellations and no-shows. Journal of Revenue and Pricing Management, 143-170. DOI: 10.1057/palgrave.rpm.5170103
  • Elmaghraby, W., & Keskinocak, P. (2003). Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions. Management Science, 1287-1309. DOI: 10.1287/mnsc.49.10.1287.17315
  • Ertuğrul, A. E., & Şahin, R. (2019). Total Revenue Boundaries for Determining Booking Limits in Airline Revenue Management. 5th International Conference on Engineering Sciences. Ankara.
  • Fiig, T., Isler, K., Hopperstad, C., & Belobaba, P. (2010). Optimization of mixed fare structures: Theory and applications. Journal of Revenue and Pricing Management, 152-170. DOI: 10.1057/rpm.2009.18
  • Frank, M., & Friedemann, M. (2009). A simulation study: comparing the Markov decision process approach to the expected marginal seat revenue b heuristic under differing demand behaviour. International Journal of Revenue Management, 133-147. DOI: 10.1504/IJRM.2009.024161
  • Gelirli, N. (2019). Analysis of Financial Implications Due to Absence of Indegenous Aircraft Leasing Enterprice in Turkey. Journal of Business, Economics and Finance, 235-246. DOI: 10.17261/Pressacademia.2019.1167
  • Hosseinalifam, M., Marcotte, P., & Savard, G. (2016). A new bid price approach to dynamic resource allocation in network revenue management. European Journal of Operational Research, 142-150. DOI: 10.1016/j.ejor.2016.04.057
  • Huanga, K., & Liang, Y.-T. (2011). A dynamic programming algorithm based on expected revenue approximation for the network revenue management problem. Transportation Research Part E: Logistics and Transportation Review, 333-341. DOI: 10.1016/j.tre.2010.11.005
  • Kramer, A., Friesen, M., & Shelton, T. (2018). Are airline passengers ready for personalized dynamic pricing? Journal of Revenue and Pricing Management, 115-120. DOI: 10.1057/s41272-017-0122-0
  • Lardeux, B., Sabatier, G., Delahaye, T., & Boudia, M. (2019). Yield optimization for airlines from ticket resell. Journal of Revenue and Pricing Management, 213-227. DOI: 10.1057/s41272-018-00167-1
  • Lee, T. C., & Hersh, M. (1993). A Model for Dynamic Airline Seat Inventory Control with Multiple Seat Bookings. Transportation Science, 252-265. DOI: 10.1287/trsc.27.3.252
  • Littlewood, K. (1972). Forecasting and Control of Passenger Bookings. AGIFORS Symposium Proc. (s. 95-117). Nathanya: AGIFORS Symposium Proc.
  • Phillips, R. L. (2005). Pricing and Revenue Optimization. Stanford, California: Stanford University Press.
  • Talluri, K., & van Ryzin, G. (2004). The Theory and Practice of Revenue Management. Boston: Kluwer Academic Publishers.
  • Tavana, H., & Weatherford, L. (2017). Application of an alternative expected marginal seat revenue method. Journal of Air Transport Management, 65-77. DOI: 10.1016/j.jairtraman.2017.02.006
  • Topaloğlu, H. (2008). A Stochastic Approximation Method to Compute Bid Prices in Network Revenue Management Problems. INFORMS Journal on Computing, 596-610. DOI: 10.1287/ijoc.1080.0269
  • Topaloğlu, H. (2009). Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management. Operations Research, 637-649. DOI: 10.1287/opre.1080.0597
  • Weatherford, L. (2004). EMSR versus EMSU: Revenue or utility? Journal of Revenue and Pricing Management, 277-284. DOI: 10.1057/palgrave.rpm.5170114
  • Wright, C. P., Groenevelt, H., & Shumsky, R. A. (2010). Dynamic Revenue Management in Airline Alliances. Transportation Science, 15-37. DOI: 10.1287/trsc.1090.0300
  • Yazdi, A. K., Kaviani, M. A., Hanne, T., & Ramos, A. (2020). A binary differential evolution algorithm for airline revenue management: a case study. Soft Computing. DOI: 10.1007/s00500-020-04790-2
  • Zhang, D., & Cooper, W. L. (2005). Dan Zhang, William L. Cooper. Operations Research, 415-431. DOI: 10.1287/opre.1050.0194
There are 29 citations in total.

Details

Primary Language English
Subjects Finance, Business Administration
Journal Section Articles
Authors

Asli Emine Ertugrul This is me 0000-0002-4104-1103

Ramazan Sahın This is me 0000-0001-7074-4038

Publication Date June 30, 2020
Published in Issue Year 2020

Cite

APA Ertugrul, A. E., & Sahın, R. (2020). A NEW APPROACH FOR AIRLINE REVENUE MANAGEMENT: TOTAL REVENUE BOUNDARIES. Journal of Business Economics and Finance, 9(2), 62-67. https://doi.org/10.17261/Pressacademia.2020.1214

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