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Scheduling railway freight delivery appointments using a bid price approach

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  • Kraft, Edwin R.

Abstract

This paper proposes a method for establishing aggressive but achievable delivery appointment times for railroad shipments, taking into account individual customer needs and forecasted available train capacity. The concept of scheduling appointment times is directly patterned after current motor carrier industry practice, so that customers can plan for rail or truck deliveries in the same way. A shipment routing problem is decomposed into a deterministic "dynamic car scheduling" (DCS) process for shipments already accepted and a stochastic "train segment pricing" (TSP) process for forecasting future demands which have not yet called in and for which delivery appointments have yet to be scheduled. Both are formulated as multi-commodity network flow (MCNF) problems, where each shipment is treated as a separate commodity. Gain coefficients represent recapture probabilities that a specific customer will accept a carrier's service offer. A comparison with a widely used revenue management formulation is given. A Lagrangian heuristic for obtaining a primal solution is also described. The problem is solved within a 1% gap using the subgradient algorithm.

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  • Kraft, Edwin R., 2002. "Scheduling railway freight delivery appointments using a bid price approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(2), pages 145-165, February.
    • Handle: RePEc:eee:transa:v:36:y:2002:i:2:p:145-165
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    Cited by:

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    2. Lawley, Mark & Parmeshwaran, Vijay & Richard, Jean-Philippe & Turkcan, Ayten & Dalal, Malay & Ramcharan, David, 2008. "A time-space scheduling model for optimizing recurring bulk railcar deliveries," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 438-454, June.
    3. Li, Dongjun & Islam, Dewan Md Zahurul & Robinson, Mark & Song, Dong-Ping & Dong, Jing-Xin & Reimann, Marc, 2024. "Network revenue management game in the railway industry: Stackelberg equilibrium, global optimality, and mechanism design," European Journal of Operational Research, Elsevier, vol. 312(1), pages 240-254.
    4. Bin Qiao & Shenle Pan & Eric Ballot, 2019. "Dynamic pricing model for less-than-truckload carriers in the Physical Internet," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2631-2643, October.
    5. Dominique Bouf & Yves Crozet & Sophie Masson & Pierre-Yves Péguy & Stéphanie Souche & Bjørnar Andreas Kvinge & Ioan Cuncev & Paola Cossu & Henning Tegner, 2003. "Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”," Post-Print halshs-00142744, HAL.
    6. Wang, Xinchang & Wang, Hua & Zhang, Xiaoning, 2016. "Stochastic seat allocation models for passenger rail transportation under customer choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 95-112.
    7. Mar-Ortiz, Julio & Castillo-García, Norberto & Gracia, María D., 2020. "A decision support system for a capacity management problem at a container terminal," International Journal of Production Economics, Elsevier, vol. 222(C).
    8. Bin Qiao & Shenle Pan & Eric Ballot, 2016. "Less-than-truckload Dynamic Pricing Model in Physical Internet," Post-Print hal-01326416, HAL.

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