Scheduling railway freight delivery appointments using a bid price approach |
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| Institution: | 1. China-EU Center for Information and Communication Technologies in Agriculture, China Agricultural University, Beijing 100083, PR China;2. Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University, Beijing 100083, PR China;3. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, PR China;4. College of Information and Electrical Engineering, Ludong University, Yantai 264025, PR China;1. Economic Department, Federal University of Juiz de Fora (UFJF), Brazil;2. Center for Regional and Development Planning, Department of Economics, Federal University of Minas Gerais, Brazil;3. Regional Economics Applications Laboratory, University of Illinois at Urbana-Champaign, United States;1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No. 3 Shangyuan Cun, Haidian District, Beijing 100044, China;2. School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuan Cun, Haidian District, Beijing 100044, China;3. Institute for Transportation Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland |
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| 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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