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Integrating capacity analysis with high-speed railway timetabling: A minimum cycle time calculation model with flexible overtaking constraints and intelligent enumeration

机译:容量分析与高速铁路时间表整合:具有灵活超车约束和智能枚举的最小周期时间计算模型

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Compared with most optimization methods for capacity evaluation, integrating capacity analysis with timetabling can reveal the types of train line plans and operating rules that have a positive influence on improving capacity utilization as well as yielding more accurate analyses. For most capacity analyses and cyclic timetabling methods, the cycle time is a constant (e.g., one or two hours). In this paper, we propose a minimum cycle time calculation (MCTC) model based on the periodic event scheduling problem (PESP) for a given train line plan, which is promising for macroscopic train timetabling and capacity analysis. In accordance with train operating rules, a non-collision constraint and a series of flexible overtaking constraints (FOCs) are constructed based on variations of the original binary variables in the PESP. Because of the complexity of the PESP, an iterative approximation (IA) method for integration with the CPLEX solver is proposed. Finally, two hypothetical cases are considered to analyze railway capacity, and several influencing factors are studied, including train regularity, train speed, line plan specifications (train stops), overtaking and train heterogeneity. The MCTC model and IA method are used to test a real-world case involving the timetable of the Beijing-Shanghai high-speed railway in China. (C) 2016 Elsevier Ltd. All rights reserved.
机译:与大多数用于容量评估的优化方法相比,将容量分析与时间表整合在一起可以揭示火车计划和运行规则的类型,这些类型对提高容量利用率和产生更准确的分析有积极影响。对于大多数容量分析和循环时间表方法,循环时间是一个常数(例如,一两个小时)。在本文中,我们针对给定的火车线路计划,基于周期性事件调度问题(PESP)提出了最小周期时间计算(MCTC)模型,这对于宏观火车时间表和容量分析是有希望的。根据列车运行规则,基于PESP中原始二进制变量的变化构造非碰撞约束和一系列灵活的超车约束(FOC)。由于PESP的复杂性,提出了一种与CPLEX求解器集成的迭代逼近(IA)方法。最后,考虑了两个假设情况来分析铁路通行能力,并研究了几个影响因素,包括火车规则性,火车速度,线路计划规格(火车停靠站),超车和火车异质性。 MCTC模型和IA方法用于测试涉及中国京沪高铁时间表的实际案例。 (C)2016 Elsevier Ltd.保留所有权利。

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