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Dynamic revenue management in a passenger rail network under price and fleet management decisions

Author

Listed:
  • Keyvan Kamandanipour

    (University of Tehran)

  • Siamak Haji Yakhchali

    (University of Tehran)

  • Reza Tavakkoli-Moghaddam

    (University of Tehran)

Abstract

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company’s historical sales data. A mixed-integer non-linear programming model is presented to maximize the company’s profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company.

Suggested Citation

  • Keyvan Kamandanipour & Siamak Haji Yakhchali & Reza Tavakkoli-Moghaddam, 2024. "Dynamic revenue management in a passenger rail network under price and fleet management decisions," Annals of Operations Research, Springer, vol. 342(3), pages 2049-2073, November.
    • Handle: RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05296-4
    DOI: 10.1007/s10479-023-05296-4
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    References listed on IDEAS

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    1. A. Ciancimino & G. Inzerillo & S. Lucidi & L. Palagi, 1999. "A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 168-181, May.
    2. Alexander Armstrong & Joern Meissner, 2010. "Railway Revenue Management: Overview and Models (Operations Research)," Working Papers MRG/0019, Department of Management Science, Lancaster University, revised Jul 2010.
    3. Christopher P. Wright & Harry Groenevelt & Robert A. Shumsky, 2010. "Dynamic Revenue Management in Airline Alliances," Transportation Science, INFORMS, vol. 44(1), pages 15-37, February.
    4. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    5. Jin Qin & Wenxuan Qu & Xuanke Wu & Yijia Zeng, 2019. "Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    6. Abbas Azadi Moghaddam Arani & Fariborz Jolai & Mohammad Mahdi Nasiri, 2019. "A multi-commodity network flow model for railway capacity optimization in case of line blockage," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 7(4), pages 297-320, October.
    7. Jin Qin & Yijia Zeng & Xia Yang & Yuxin He & Xuanke Wu & Wenxuan Qu, 2019. "Time-Dependent Pricing for High-Speed Railway in China Based on Revenue Management," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    8. Dimitris Bertsimas & Ioana Popescu, 2003. "Revenue Management in a Dynamic Network Environment," Transportation Science, INFORMS, vol. 37(3), pages 257-277, August.
    9. You, Peng-Sheng, 2008. "An efficient computational approach for railway booking problems," European Journal of Operational Research, Elsevier, vol. 185(2), pages 811-824, March.
    10. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    11. Dillenberger, Christof & Escudero, Laureano F. & Wollensak, Artur & Zhang, Wu, 1994. "On practical resource allocation for production planning and scheduling with period overlapping setups," European Journal of Operational Research, Elsevier, vol. 75(2), pages 275-286, June.
    12. Fuentes, Manuel & Cadarso, Luis & Marín, Ángel, 2019. "A hybrid model for crew scheduling in rail rapid transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 248-265.
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