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Special Issue — Selected Papers from the International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT2017); Guest Editors: Y. Qin, M. An and L. M. JiaNo Access

An Optimization Method for Train Seat Inventory Control

    https://doi.org/10.1142/S0218194018400077Cited by:0
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    Abstract

    Railway passenger transportation plays a fundamental role in China, reasonable revenue is the guarantee of railway's regularly development such as equipment replacement, technology enhancement, etc. Although many studies on the railway revenue models have been conducted, there is a lack of effective modeling which considers the multiple trains and the multiple levels of seats in real operation. Aiming to improve the revenue of railway transportation industry, this paper proposes a new optimization method for the train seat inventory control problem with the consideration of the multiple trains and the multiple levels of seats. As the in-depth research of this problem, an integer linear programming model is formulated which aims to maximize the total revenue of rail industry. The commercial software MATLAB with CPLEX solver is employed to obtain the approximate optimal solutions. The effectiveness and performance of the proposed approaches are testified by two examples implemented on a simple railway corridor and Wuhan–Guangzhou high-speed railway corridor. Moreover, sensitivity analysis experiments are given to explore the impact on the revenue if the model parameters are changed.

    References

    • 1. L. Yang, J. Qi, S. Li and Y. Gao, Collaborative optimization for train scheduling and train stop planning on high-speed railways, Omega 64 (2016) 57–76. Crossref, ISI, Google Scholar
    • 2. X. Xu, K. Li and L. Yang, Scheduling heterogeneous train traffic on double tracks with efficient dispatching rules, Transport. Res. Part B 780 (2015) 364–384. Crossref, Google Scholar
    • 3. J. Qi, L. Yang, Y. Gao, S. Li and Y. Gao, Integrated multi-track station layout design and train scheduling models on railway corridors, Transport. Res. Part C 69 (2016) 91–119. Crossref, ISI, Google Scholar
    • 4. A. Ciancimino, G. Inzerillo, S. Lucidi et al., A mathematical programming approach for the solution of the railway yield management problem, Transport. Sci. 33 (2) (1999) 168–181. Crossref, ISI, Google Scholar
    • 5. P. You, An efficient computational approach for railway booking problems, European J. Oper. Res. 185 (2) (2008) 811–824. Crossref, ISI, Google Scholar
    • 6. K. Talluri and G. Van Ryzin, Revenue management under a general discrete choice model of consumer behavior, Manag. Sci. 50 (1) (2004) 15–33. Crossref, ISI, Google Scholar
    • 7. A. Armstrong and J. Meissner, Railway revenue management: Overview and models, Depart. Manag. Sci. 1 (2010) 1–23. Google Scholar
    • 8. M. Hersh and S. P. Ladany, Optimal seat allocation for flights with one intermediate stop, Comput. Oper. Res. 5 (1978) 31–37. Crossref, ISI, Google Scholar
    • 9. E. L. Williamsom, Airline Network Seat Inventory Control: Methodologies and Revenue Impacts, PhD thesis, MIT, 1992. Google Scholar
    • 10. T. H. Tsai, C. K. Lee and C. H. Wei, Neural network based temporal feature models for short-term railway passenger demand forecasting, Expert Syst. Appl. 36 (2) (2009) 3728–3736. Crossref, ISI, Google Scholar
    • 11. J. Xie, Y. Zhang and Z. Wang, Railway freight volume forecasting based on unbiased grey-fuzzy-Markov chain method, J. China Railway Soc. 31 (1) (2009) 1–7. Google Scholar
    • 12. L. Hou and G. Ma, Forecast of railway passenger traffic based on a grey linear regression combined model, Comput. Simul. 7 (2011). Google Scholar
    • 13. Y. Huo and G. Zhang, Overbooking revenue model and its simplification, Indust. Eng. J. Guangzou 8 (5) (2005) 96. Google Scholar
    • 14. P. Belobaba, Yield management, overbooking, and pricing, Oper. Res. 37 (2) (1989) 183–197. Crossref, ISI, Google Scholar
    • 15. J. Xia and Z. Li, Model for airline overbooking based on binomial distribution, J. Civel Aviation Univ. China 1 (2006) 13. Google Scholar
    • 16. T. Li, Informedness and customer-centric revenue management, 2009. Google Scholar
    • 17. J. Bi, The application of revenue management in railway ticket assignment, Chin. Railways 1 (2013) 45–48. Google Scholar
    • 18. Q. Chen and G. Yang, Research on optimization method for emu seat inventory control, J. Lanzhou Jiaotong Univ. 29 (1) (2010) 66–68. Google Scholar
    • 19. L. Chen, Preliminary probe into economic mathematics model of system of multistage ticket price implemented by airlines, J. Civil Aviation Univ. China 25 (4) (2007) 40–43. Google Scholar
    • 20. Y. Bao, The Theory and Methods for Railway Seat Inventory Control (Beijing Jiaotong University, Beijing, 2014). Google Scholar
    • 21. B. Lan and L. Zhang, Revenue management model for high-speed passenger railway, Chinese J. Manag. Sci. 17 (4) (2009) 53–59. Google Scholar
    • 22. H. Wang, X. Lu and L. Zhou, Intelligent seat allotmen method for railway passenger train based on passenger flow forecast, Chinese Railway Sci. 34 (3) (2013) 128–132. Google Scholar
    • 23. F. Shi, Y. Chen and W. Zhou, Railway passenger train seats allotment plan establishment and evaluation method based on user equilibrium analysis, China Railway Sci. 29 (6) (2008) 98–103. Google Scholar
    • 24. Y. Luo, J. Liu and Q. Lai, Optimization of seat inventory allocation for passenger trains with first-come-serve seats, J. China Railway Soc. 38 (5) (2016) 11–15. Google Scholar
    • 25. H. Fu, B. R. Sperry and L. Nie, Operational impacts of using restricted passenger flow assignment in high-speed train stop scheduling problem, Math. Probl. Eng. 78 (70) (2013) 143–175. Google Scholar
    • 26. H. Fu and L. Nie, Economy and efficiency oriented passenger flow organization: Train stop scheduling for high-speed railways, J. Beijing Inst. Technol. 20 (2011) 79–83. Google Scholar
    • 27. H. Wang, X. Lu and L. Zhou, Intelligent seat allotmen method for railway passenger train based on passenger flow forecast, Chin. Railway Sci. 34 (3) (2013) 128–132. Google Scholar
    • 28. S. Zhang, Research on Strategies of Railway Passenger Train Tickets Distribution for Instant Booking & Advance Booking System (Lanzhou Jiaotong University, Lanzhou, 2007). Google Scholar
    • 29. R. Zhang, High-Speed Railway Passenger Ticket Assignment Model and Its Applications Study Based on Revenue Management (Beijing Jiaotong University, Beijing, 2015). Google Scholar
    • 30. J. Zong and Z. Li, Study on the model of railway passenger ticket distribution under different discounts, Railway Transport Econ. 30 (7) (2008) 24–26. Google Scholar
    • 31. Y. Tang, Application of Revenue Management in China Railway Operation (Southwest Jiaotong University, Chengdu, 2008). Google Scholar
    • 32. F. Shi, G. Zheng and Q. Gu, Optimal dynamic pricing of railway passenger ticket, J. China Railway Soc. 24 (1) (2002) 1–4. Google Scholar
    • 33. P. You, Dynamic pricing in airline seat management for flights with multiple flight legs, Transportation Sci. 40 (1999) 26–37. Google Scholar
    • 34. T. Koide and H. Ishii, The hotel yield management with two types of room prices, overbooking and cancellations, Int. J. Product. Econ. 93 (2005) 417–428. Crossref, ISI, Google Scholar
    • 35. J. Zhang and X. Wang, A constraint solver and its application to path feasibility analysis, Int. J. Softw. Eng. Knowl. Eng. 11 (2) (2001) 139–156. Link, ISI, Google Scholar
    • 36. R. Merkel, D. Wang, H. Lin et al., Automatic verification of optimization algorithms: A case study of a quadratic assignment problem solver, Int. J. Softw. Eng. Knowl. Eng. 21 (2) (2011) 289–307. Link, ISI, Google Scholar
    • 37. B. Xu, The Stop Schedule Plan of High Speed Railway (Beijing Jiaotong University, Beijing, 2012). Google Scholar
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