Optimization Model of Railway Wagon Flow Path with Uncertain Demand

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作者
Zhao, Yinan [1 ]
Lin, Boliang [2 ]
机构
[1] School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang, Hebei,050043, China
[2] School of Traffic and Transportation, Beijing Jiaotong University, Beijing,100044, China
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The robust optimization in the uncertainty theory is introduced to describe the traffic flow fluctuation in the daily transportation organization work of railway freight transport; and an optimization model of railway wagon flow path with uncertain demand is proposed. First; considering the constraints of line carrying capacity and network flow balance; setting a detour rate threshold parameter to measure the rationality of the wagon flow path mileage; and regarding the minimization of the travel cost as the optimization target; the optimization model of wagon flow path with the fixed demand is established. Then; the traffic flow fluctuation is described by introducing the box uncertainty set; the conservatism of the model is adjusted by designing the price of robustness; and the optimization model of wagon flow path with uncertain demand is constructed based on robust counterpart theory. Finally; the validity of the model is verified by a small_scale example; and the practicability of the model is verified by a real freight transportation network in Shenyang Railway Bureau Group Co; Ltd. The results show that the corresponding optimal path plan changes when part of the traffic flow fluctuates. The price of robustness can balance the optimal plan cost and the probability of violating the carrying capacity constraint. When the price of robustness is 5 and 11 in the small_scale example and real example respectively; the obtained wagon flow path plan is at the balance point between the system cost and the transportation demand satisfaction. This model and robust optimization theory can provide decision_making reference for managers to make the appropriate plan of wagon flow path. © 2024 Chinese Academy of Railway Sciences. All rights reserved;
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页码:190 / 202
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