A dynamic model for railway freight overbooking

Fen-ling Feng , Jia-qi Zhang , Xiao-feng Guo

Journal of Central South University ›› 2015, Vol. 22 ›› Issue (8) : 3257 -3264.

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Journal of Central South University ›› 2015, Vol. 22 ›› Issue (8) : 3257 -3264. DOI: 10.1007/s11771-015-2864-4
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A dynamic model for railway freight overbooking

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Abstract

In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process (dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation (FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.

Keywords

revenue management / railway freight / overbooking / dynamic model

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Fen-ling Feng, Jia-qi Zhang, Xiao-feng Guo. A dynamic model for railway freight overbooking. Journal of Central South University, 2015, 22(8): 3257-3264 DOI:10.1007/s11771-015-2864-4

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References

[1]

McgillJ, VanryzinG. Revenue management: Research overview and prospects [J]. Transportation Science, 1999, 33(2): 233-256

[2]

KimesS. Restaurant revenue management: Could it work? [J]. Journal of Revenue & Pricing Management, 2005, 4(1): 95-97

[3]

SieragD D, KooleG M, van der MEIRD, van der RESTJ I, ZwartB. Revenue management under customer choice behaviour with cancellations and overbooking [J]. European Journal of Operational Research, 2015, 246: 170-185

[4]

BharillR, RangarajN. Revenue management in railway operations: A study of the Rajdhani express, Indian railways [J]. Transportation Research Part A: Policy and Practice, 2008, 42(9): 1195-1207

[5]

FengF-l, LanDan. Prediction of railway cargo carrying capacity in China based on system dynamics [J]. Procedia Engineering, 2012, 29: 597-602

[6]

TsaiT-h, LeeC-k, WeiC-hung. Neural network based temporal feature models for short-term railway passenger demand forecasting [J]. Expert Systems with Applications, 2009, 36: 3728-3736

[7]

TsaiT-hsien. A self-learning advanced booking model for railway arrival forecasting [J]. Transportation Research Part C: Emerging Technologies, 2014, 39: 80-93

[8]

KraftE. Scheduling railway freight delivery appointments using a bid price approach [J]. Transportation Research Part A: Policy and Practice, 2002, 36(2): 145-165

[9]

CrevierB, CordeauJ, SavardG. Integrated operations planning and revenue management for rail freight transportation [J]. Transportation Research Part B: Methodological, 2012, 46(1): 100-119

[10]

GopalakrishnanR, RangarajN. Capacity management on long-distance passenger trains of Indian railways [J]. Interfaces, 2010, 40(4): 291-302

[11]

HetrakulP, CirilloC. A latent class choice based model system for railway optimal pricing and seat allocation [J]. Transportation Research Part E: Logistics and Transportation Review, 2014, 61: 68-83

[12]

LanY-j, BallM, KaraesmenI. Overbooking and fare-class allocation with limited information [R]. Smith R H.School Research Paper, No RHS, 200706-055

[13]

BeckmannM. Decision and team problems in airline reservations [J]. Econometrica: Journal of the Econometric Society, 1958134-145

[14]

AmaruchkulK, Sae-LimP. Airline overbooking models with misspecification [J]. Journal of Air Transport Management, 2011, 17(2): 143-147

[15]

LanY-j, BallM, KaraesmenI. Regret in overbooking and fare-class allocation for single leg [J]. Manufacturing & Service Operations Management, 2011, 13(2): 194-208

[16]

RothsteinM. An airline overbooking model [J]. Transportation Science, 1971, 5(2): 180-192

[17]

Moussawi-HaidarL. Optimal solution for a cargo revenue management problem with allotment and spot arrivals [J]. Transportation Research Part E: Logistics and Transportation Review, 2014, 72: 173-191

[18]

HuangY-q, GeY-m, ZhangX-d, XuY-fan. Overbooking for parallel flights with transference [J]. International Journal of Production Economics, 2013, 144(2): 582-589

[19]

ChatwinR. Continuous-time airline overbooking with time-dependent fares and refunds [J]. Transportation Science, 1999, 33(2): 182-191

[20]

GeY-m, XuY-f, DaiYue. Overbooking with bilateral transference in parallel flights [J]. International Journal of Production Economics, 2010, 128(2): 577-585

[21]

ChenX, HaoGang. Co-opetition alliance models of parallel flights for determining optimal overbooking policies [J]. Mathematical and Computer Modelling, 2013, 57(5–6): 1101-1111

[22]

HuangY-q, GeY-m, ZhangX-d, XuY-fan. Overbooking for parallel flights with transference [J]. International Journal of Production Economics, 2013, 144(2): 582-589

[23]

FengF-l, ZhangQ-ya. Multimodal transport system coevolution model based on synergetic theory [J]. Discrete Dynamics in Nature and Society, 2015

[24]

FengF-l, TangZ-w, WangLei. A fault tolerance optimization model of the china railway geographic network topological structure [J]. Discrete Dynamics in Nature and Society, 2015

[25]

LiH-j, ShakedM. Stochastic convexity and concavity of Markov processes [J]. Mathematics of Operations Research, 1994, 19(2): 477-493

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