交通运输系统工程与信息 ›› 2018, Vol. 18 ›› Issue (1): 186-192.

• 系统工程理论与方法 • 上一篇    下一篇

停站约束下多列车运力分配与定价联合决策模型

江文辉1a,徐菱* 1a, 1b ,李延来1a, 1b,李思雯1a,丁小东2   

  1. 1. 西南交通大学 a. 交通运输与物流学院,b. 综合交通运输智能化国家地方联合工程实验室,成都 610031; 2. 中国铁道科学研究院 运输与经济研究所,北京 100081
  • 收稿日期:2017-09-18 修回日期:2017-12-03 出版日期:2018-02-25 发布日期:2018-02-26
  • 作者简介:江文辉(1992-),男,河南驻马店人,博士生.
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China(71371156);铁路总公司科技研究开发计划项目/ The Science Research and Development Program of China Railway(2013X009-A-1-2);四川省重点科技计划项目/The Key Science and Technology Program of Sichuan Provice(2014GZ0019).

Capacity Allocation and Pricing Joint Decision Model for Multiple Train Considering Train Stop Constraints

JIANG Wen-hui1a, XU Ling1a, 1b, LI Yan-lai1a, 1b, LI Si-wen1a, DING Xiao-dong2   

  1. 1a. School of Transportation & Logistic, 1b. National and Combined Engineering Lab of Intelligentizing Integrated Transportation, Southwest Jiaotong University, Chengdu 610031, China; 2. Transportation & Economics Research Institute, China Academy of Railway Science, Beijing 100081, China
  • Received:2017-09-18 Revised:2017-12-03 Online:2018-02-25 Published:2018-02-26

摘要:

基于收益管理的思想将铁路货运市场分为合同市场和自由市场,针对铁路运输网 络中每个OD,合同市场的运力需求服从正态分布,自由市场的运力需求表现为价格的反应函 数并辅以随机变量来反映需求的波动性.同时考虑列车的停站约束条件,以列车的停站方案、2 个市场运力分配方案和自由市场的运价为决策变量,构建多列车运力分配和定价联合决策的 混合整数概率非线性规划模型,利用粒子群算法对模型求解,通过算例验证了模型和算法的 有效性.最后以双市场统一定价策略为对比方案,结果表明,本文所建立的模型可有效提高收 益,且自由市场需求波动越大,收益优化越显著.

关键词: 铁路运输, 运力分配与定价决策, 收益管理, 停站约束, 粒子群算法

Abstract:

Based on the idea of revenue management, the railway freight market is divided into allotment market and spot market. For each origin-destination itinerary in the rail transport network, it is assumed that the demand for allotment market is a normally distributed random variable, and the demand for spot market is expressed as the linear function of price and is supplemented by a random variable to reflect the volatility of demand. Considering the train stop constraints, the mixed integer probabilistic nonlinear programming model of capacity allocation and pricing joint decision is established with train stop schedule plan, capacity alloction program and pricing program of spot market as decision variables, which is solved by the particle swarm algorithm, then the model and the algorithm are verufied by an example. Finally, compared with the unified pricing strategy of two markets, the results show that the model establised in this parper can effectively increase revenue, and revenue optimization is more pronounced as demand fluctuations increasing in spot market.

Key words: railway transportation, capacity allocation and pricing decision, revenue management, train stop constraints, particle swarm optimization

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