分组列车优化组织理论与方法研究
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摘要
车流组织是铁路行车组织工作中的重要内容,一直也是国内外铁路行业专家以及运筹管理领域学者们研究的课题之一。由于铁路运输组织的行业特殊性以及网络大规模尺度,指导运输生产的各种计划和方案相互关联,相互影响,从而使得车流组织优化非常复杂也非常困难,至今仍未圆满地解决。分组列车作为其中的一种车流组织形式,在以美国和加拿大为代表的铁路货运发达国家非常普遍。但由于铁路管理体制的不同以及运输组织模式的差异等其他方面原因,国外的研究成果难以完全应用于我国实际。而在国内,车流组织研究主要集中于单组列车,很少涉及分组列车；实际生产中分组列车的比例也非常低,目前主要在牵引定数递减情形以及集装箱五定班列中有过采用,但并不广泛。因此,分组列车的理论成果和实践经验还非常缺乏。
相比单组列车,分组列车组织较为复杂,要求编成站具有较大的改编能力和较多的调车线路等。尽管如此,仍有其有利性存在,如有利于保证列车满轴正点和不违编,远程车流在换挂站部分改编而停留时间较短,减轻沿途技术站的负担等。既然分组和单组列车各有利弊,那么二者相互配合,扬长避短,发挥各自的优势对提高运输效率和效益将很有现实意义。另外,在列车的构成上,单组列车和摘挂列车都可以视为特殊的分组列车。从这个角度来说,分组列车的内涵更大,其理论研究也更具普适性。因此,对分组列车组织的相关问题研究,具有实际应用价值和学术研究价值。
本论文在分析铁路车流组织原理、总结国内外编组计划优化方面的理论和方法的基础上,运用系统工程原理以及微观机理融合宏观机制的指导思想,采用定性分析与定量测度相结合,理论推导与仿真实验相融合的方法,比较系统地研究了分组列车组织特征、集结特性、组织条件、编组方案优化等相关问题,主要研究工作包括以下几个方面：
(1)既有文献的对比分析。在总结大量相关文献的基础上,从研究对象、建模方法、求解算法三个方面分别介绍国内外编组计划的研究现状,对比并评述国内外编组计划在编制流程、构成内容和建模方法三个方面的特点。
(2)分组列车车流组织特征分析。在对车流组织的含义、内容、组织方案以及我国车流组织经验概述的基础上,分析了分组列车的基本特征,包括技术作业特点,编成站、换挂站和终到站的作业流程,货车构成,利弊分析,组织条件分析以及各种组织形式分类,并与单组列车进行了相应地对比。
(3)分组列车集结特性研究。以双组形式又尤以其中的固定重量形式分组列车为研究对象,对其集结特性进行定性分析和定量测度。将固定重量形式分组列车的总集结耗费划分为固有和附加两类,根据集结过程的动态特性和车流到达的不确定性,将到达批中的车辆数和间隔时间都视为随机变量,在独立同分布的假定下,描述固定形式分组列车在编成站和换挂站的集结过程。进一步假定到达批中的车辆数服从泊松分布以及间隔时间服从指数分布,应用随机过程知识,理论推导了集结特性的三个表现方面：集结批次、集结占用时间以及集结消耗,采用数值计算方法分析了单参数的灵敏度以及双参数的耦合影响,并给出三者均值的估计公式。
(4)分组列车组织条件研究。也以双组形式的固定车组重量分组列车为研究对象,其组织条件主要包括列车编成辆数最佳分配和开行适用条件。对于前者,将其描述为,在车流到达特征已定的条件下,如果车流量递减,通过确定基本组和补轴组重量的最佳组合,使得平均每列车的集结耗费最小或者集结占用时间最小,建立整数优化模型。模型采用分阶段逐步求解思路,首先通过数值计算探究最佳固定重量的影响机制,然后基于挖掘的信息采用回归方法给出经验计算公式。对于后者,基于固定形式分组列车与单组列车集结耗费的比较分析,以前者相对后者的总净节省作为其综合效益的度量,构建了其开行适用性条件,并采用离散事件系统仿真方法进行验证。
(5)分组列车编组方案优化参数。研究了分组列车编组方案的优化参数,具体包括计划车流量,固定和非固定形式的集结参数,部分改编相对无改编通过增加时间、相对完全改编的减少时间两个节省时间参数,途中改编次数,以及最大车流组号数。
(6)分组列车编组计划优化模型。以途中仅换挂一次的双组列车为研究对象,在同时采用分组和单组列车形式进行车流组织的框架下,根据编组去向在车流和列流之间的过渡和衔接角色,重构车流组织任务,定性分析综合编组方案的内容和特点。基于车流组织任务的分解和我国的实际情况,忽略列车接续子问题,进而以路网编组去向方案、编组去向接续方案以及路网列流方案为0-1决策变量；考虑物理方面的车站调车线数约束和改编能力约束,车流组织制度方面的车流接续归并和车流不拆散约束,以及决策变量之间的逻辑约束；以列车集结和车流改编的总耗费最小为目标函数,建立车流径路已知条件下综合编组方案优化模型OMITFP。针对模型的特点,对其中编组去向接续子问题(CBAP),以路网编组去向方案和编组去向接续方案为0-1决策变量,考虑避圈约束、最大改编次数约束、决策变量之间的逻辑约束以及筛除显然不利方案,构建数学模型并设计了方案树法和回溯算法。
As one of the research hotspots for railway experts and scholars in operational research (OR) and management science at home and abroad, wagon flow organization plays an important role in train operation. Owing to the particularity of railway transportation operation and the large-scale network attribute, kinds of plans and schemes conducting transportation production are correlated and interchanged with each other, which makes this problem so extraordinarily difficulty and complicated that it has not been resolved completely so far.Multi-block train, awagon flow organization mode, is popular and widespread in U.S. and Canada representative of the countries with developed railway freight transportation. However, it's difficult to fully applied abroad achievements to our country because of the difference in management system, transportation organization model, etc. In China, the research on wagon flow organization mainly focuses on one-block train (single group train) and rarely involves multi-block train, as well as the latter is of a very low proportion in practice and just adopted in traction destiny decline situation and "Five Scheduled" container trains at present. Therefore, theoretical achievements and practical experience of multi-block train are still scarce.
Comparing with one-block train, the operation of multi-block train is more complex due to requiring the formation station with larger resorting capacity and more reclassification tracks, etc. However, it also has its own advantages, such as guaranteeing full train tonnagepunctuality and complying with train formation plan (TFP), shortening the transfer delay for long-distance wagon partly-resorting in intermediate block-swap station(s), reducing the load of technical stations along the route, etc. Considering the pros and cons of the two modes, it will be practically meaningful to improve efficiency and benefit of transportation by cooperating, maximizing the favorable factors and minimizing unfavorable ones. Moreover, one-block train and pick-up and drop train can be regarded as special multi-block trains in term of train's constitution. From this point of view, multi-block train has wider extension, and the corresponding theoretical research is more universal. So it is valuable in both practice and academy to research the related problems of multi-block tram.
Based on the analysis the principles of wagon flow organization and the summary of optimization theories and methods of TFP at home and abroad, following the guiding ideology of system engineering and micro-mechanism fusing with macro-mechanism, this paper systematically probed into the problems of operation characteristics, accumulation properties, operation conditions, train formation plan optimization, etc, by adopting qualitative and quantitative analysis together with deduction and simulation method. The main research work is as follows:
(1) The comparative analysis of existing literature. On the basis of summing up related literature, the research status of TFP at home and abroad was introduced from the aspects of research object, modeling method and solution algorithm and their respective characteristics were contrasted and reviewed in terms of conducting process, composition content and modeling method.
(2) Analysis of the operation features of multi-block train. Base on the overview of definition, content, operation scheme and experience of wagon flow organization, in China, some characteristics including technical operating traits, operation process in formation station, intermediate block-swap station(s) and destination, cars' categorical attributes, advantages and disadvantages, operation conditions and various types of multi-block train mode were analyzed and contrasted with one-block mode.
(3) Study on the accumulation properties of multi-block train. Taking double-block train with fixed weight as the object, qualitative analysis and quantitative measure were conducted aiming at the accumulation properties of multi-block train. Dividing the total aggregation cost into the inherent and additional aggregation cost, according to the uncertainty for arrival and the dynamics for aggregation process, the aggregation process of the fixed mode multi-block train at the formation station and intermediate block-swap station(s) were described by considering the size and interval time of the batch of cars as random variables and assuming them to independent and identical distribution. Furthermore, assuming that the size of the batch of cars obeys a Poisson distribution and the interval time follows an exponential one, the performance of aggregation process, i.e., aggregation batch, aggregation occupation time and aggregation cost were deduced applying stochastic processes theory. With the numerical method, estimation formulas of their mean were obtained while analyzing single parameter's sensitivity and the coupling influence of double-parameter.
(4) Study on the operation conditions of multi-block train. The double-block train with fixed weight was chosen as concern object yet whose operation conditions embrace the determination of train constitution and applicable operation conditions. The former was modeled as determinating the optimal proportion between basic-block (i.e. long-distance block) and complementary-block (i.e. short-distance block) to minimize the aggregation occupation time or aggregation cost given the arrival characteristics of the wagon flow. An Integer optimization model was established and solved though two phases. In phase I, the effect mechanism of optimal weights was mined using numerical calculation. In phase Ⅱ, the corresponding empirical regression formulas were respectively provided according to the explored information. Based on the analysis and comparison of aggregation cost between multi-block train with fixed weight and one-block train, applicable operation conditions of the latter was built by adopting the total net savings between the former and the latter as the measure of its comprehensive benefits and tested using discrete event system simulation (DESS) method.
(5) The optimization parameters of multi-block train formation plan. This section studied the parameters for multi-block train formation plan optimization, including the traffic flow in schedule length (planning horizon), accumulation parameters of fixed and non-fixed pattern, two time-saving parameters about block swap that one is relative delay corresponding to passing through and the other is relative saving corresponding to resorting, the up-bound of reclassifi cation and maximum possible blocks in every station.
(6) The optimization model of multi-block train formation plan. Taking the double-block train which has only one block-swap on its route as the research object in this section, under the framework by providing multi-block train and the one-block train services at the same time, the task of wagon flow organization has been reconstructed and the contents and features of the integrated train formation plan (ITFP) have been qualitatively analyzed according to the transitional linkage of blocks between cars and trains. Considering the decomposition of the above task and the actual situation of China, the block-to-train assignment plan (BTAP) was ignored, and then the block network design plan (BNDP), car-to-block assignment plan (CBAP) and train network design plan (TNDP) were taken as0-1decision variables.In order to minimize the total delay cost generated by aggregation, reclassification and transfer in all itineraries, an optimization model of integrated train formation plan (OMITFP) in the situation of giving car routing was constructed while satisfying the limitation in physical aspects, i.e., resorting capacity and reclassification tracks for every terminals, restriction on operational rules and regulations for wagon-flow, i.e., non-separation and consolidation (every railcar originating or re-blocked at a yard and destined for another must travel in a block to the same next re-blocking yard), as well as logical constraints among the decision variables, Considering the model's characteristics, when two0-1decision variables corresponding to block plan and car-to-block assignment were introduced, a model to generating all possible CBAPs was developed with the constraints of non-circuit, maximum frequency of reclassification, cross relation and cutting off unfavorable plans. Besides, the method of scheme tree and backtracking algorithm was designed.
相比单组列车,分组列车组织较为复杂,要求编成站具有较大的改编能力和较多的调车线路等。尽管如此,仍有其有利性存在,如有利于保证列车满轴正点和不违编,远程车流在换挂站部分改编而停留时间较短,减轻沿途技术站的负担等。既然分组和单组列车各有利弊,那么二者相互配合,扬长避短,发挥各自的优势对提高运输效率和效益将很有现实意义。另外,在列车的构成上,单组列车和摘挂列车都可以视为特殊的分组列车。从这个角度来说,分组列车的内涵更大,其理论研究也更具普适性。因此,对分组列车组织的相关问题研究,具有实际应用价值和学术研究价值。
本论文在分析铁路车流组织原理、总结国内外编组计划优化方面的理论和方法的基础上,运用系统工程原理以及微观机理融合宏观机制的指导思想,采用定性分析与定量测度相结合,理论推导与仿真实验相融合的方法,比较系统地研究了分组列车组织特征、集结特性、组织条件、编组方案优化等相关问题,主要研究工作包括以下几个方面：
(1)既有文献的对比分析。在总结大量相关文献的基础上,从研究对象、建模方法、求解算法三个方面分别介绍国内外编组计划的研究现状,对比并评述国内外编组计划在编制流程、构成内容和建模方法三个方面的特点。
(2)分组列车车流组织特征分析。在对车流组织的含义、内容、组织方案以及我国车流组织经验概述的基础上,分析了分组列车的基本特征,包括技术作业特点,编成站、换挂站和终到站的作业流程,货车构成,利弊分析,组织条件分析以及各种组织形式分类,并与单组列车进行了相应地对比。
(3)分组列车集结特性研究。以双组形式又尤以其中的固定重量形式分组列车为研究对象,对其集结特性进行定性分析和定量测度。将固定重量形式分组列车的总集结耗费划分为固有和附加两类,根据集结过程的动态特性和车流到达的不确定性,将到达批中的车辆数和间隔时间都视为随机变量,在独立同分布的假定下,描述固定形式分组列车在编成站和换挂站的集结过程。进一步假定到达批中的车辆数服从泊松分布以及间隔时间服从指数分布,应用随机过程知识,理论推导了集结特性的三个表现方面：集结批次、集结占用时间以及集结消耗,采用数值计算方法分析了单参数的灵敏度以及双参数的耦合影响,并给出三者均值的估计公式。
(4)分组列车组织条件研究。也以双组形式的固定车组重量分组列车为研究对象,其组织条件主要包括列车编成辆数最佳分配和开行适用条件。对于前者,将其描述为,在车流到达特征已定的条件下,如果车流量递减,通过确定基本组和补轴组重量的最佳组合,使得平均每列车的集结耗费最小或者集结占用时间最小,建立整数优化模型。模型采用分阶段逐步求解思路,首先通过数值计算探究最佳固定重量的影响机制,然后基于挖掘的信息采用回归方法给出经验计算公式。对于后者,基于固定形式分组列车与单组列车集结耗费的比较分析,以前者相对后者的总净节省作为其综合效益的度量,构建了其开行适用性条件,并采用离散事件系统仿真方法进行验证。
(5)分组列车编组方案优化参数。研究了分组列车编组方案的优化参数,具体包括计划车流量,固定和非固定形式的集结参数,部分改编相对无改编通过增加时间、相对完全改编的减少时间两个节省时间参数,途中改编次数,以及最大车流组号数。
(6)分组列车编组计划优化模型。以途中仅换挂一次的双组列车为研究对象,在同时采用分组和单组列车形式进行车流组织的框架下,根据编组去向在车流和列流之间的过渡和衔接角色,重构车流组织任务,定性分析综合编组方案的内容和特点。基于车流组织任务的分解和我国的实际情况,忽略列车接续子问题,进而以路网编组去向方案、编组去向接续方案以及路网列流方案为0-1决策变量；考虑物理方面的车站调车线数约束和改编能力约束,车流组织制度方面的车流接续归并和车流不拆散约束,以及决策变量之间的逻辑约束；以列车集结和车流改编的总耗费最小为目标函数,建立车流径路已知条件下综合编组方案优化模型OMITFP。针对模型的特点,对其中编组去向接续子问题(CBAP),以路网编组去向方案和编组去向接续方案为0-1决策变量,考虑避圈约束、最大改编次数约束、决策变量之间的逻辑约束以及筛除显然不利方案,构建数学模型并设计了方案树法和回溯算法。
As one of the research hotspots for railway experts and scholars in operational research (OR) and management science at home and abroad, wagon flow organization plays an important role in train operation. Owing to the particularity of railway transportation operation and the large-scale network attribute, kinds of plans and schemes conducting transportation production are correlated and interchanged with each other, which makes this problem so extraordinarily difficulty and complicated that it has not been resolved completely so far.Multi-block train, awagon flow organization mode, is popular and widespread in U.S. and Canada representative of the countries with developed railway freight transportation. However, it's difficult to fully applied abroad achievements to our country because of the difference in management system, transportation organization model, etc. In China, the research on wagon flow organization mainly focuses on one-block train (single group train) and rarely involves multi-block train, as well as the latter is of a very low proportion in practice and just adopted in traction destiny decline situation and "Five Scheduled" container trains at present. Therefore, theoretical achievements and practical experience of multi-block train are still scarce.
Comparing with one-block train, the operation of multi-block train is more complex due to requiring the formation station with larger resorting capacity and more reclassification tracks, etc. However, it also has its own advantages, such as guaranteeing full train tonnagepunctuality and complying with train formation plan (TFP), shortening the transfer delay for long-distance wagon partly-resorting in intermediate block-swap station(s), reducing the load of technical stations along the route, etc. Considering the pros and cons of the two modes, it will be practically meaningful to improve efficiency and benefit of transportation by cooperating, maximizing the favorable factors and minimizing unfavorable ones. Moreover, one-block train and pick-up and drop train can be regarded as special multi-block trains in term of train's constitution. From this point of view, multi-block train has wider extension, and the corresponding theoretical research is more universal. So it is valuable in both practice and academy to research the related problems of multi-block tram.
Based on the analysis the principles of wagon flow organization and the summary of optimization theories and methods of TFP at home and abroad, following the guiding ideology of system engineering and micro-mechanism fusing with macro-mechanism, this paper systematically probed into the problems of operation characteristics, accumulation properties, operation conditions, train formation plan optimization, etc, by adopting qualitative and quantitative analysis together with deduction and simulation method. The main research work is as follows:
(1) The comparative analysis of existing literature. On the basis of summing up related literature, the research status of TFP at home and abroad was introduced from the aspects of research object, modeling method and solution algorithm and their respective characteristics were contrasted and reviewed in terms of conducting process, composition content and modeling method.
(2) Analysis of the operation features of multi-block train. Base on the overview of definition, content, operation scheme and experience of wagon flow organization, in China, some characteristics including technical operating traits, operation process in formation station, intermediate block-swap station(s) and destination, cars' categorical attributes, advantages and disadvantages, operation conditions and various types of multi-block train mode were analyzed and contrasted with one-block mode.
(3) Study on the accumulation properties of multi-block train. Taking double-block train with fixed weight as the object, qualitative analysis and quantitative measure were conducted aiming at the accumulation properties of multi-block train. Dividing the total aggregation cost into the inherent and additional aggregation cost, according to the uncertainty for arrival and the dynamics for aggregation process, the aggregation process of the fixed mode multi-block train at the formation station and intermediate block-swap station(s) were described by considering the size and interval time of the batch of cars as random variables and assuming them to independent and identical distribution. Furthermore, assuming that the size of the batch of cars obeys a Poisson distribution and the interval time follows an exponential one, the performance of aggregation process, i.e., aggregation batch, aggregation occupation time and aggregation cost were deduced applying stochastic processes theory. With the numerical method, estimation formulas of their mean were obtained while analyzing single parameter's sensitivity and the coupling influence of double-parameter.
(4) Study on the operation conditions of multi-block train. The double-block train with fixed weight was chosen as concern object yet whose operation conditions embrace the determination of train constitution and applicable operation conditions. The former was modeled as determinating the optimal proportion between basic-block (i.e. long-distance block) and complementary-block (i.e. short-distance block) to minimize the aggregation occupation time or aggregation cost given the arrival characteristics of the wagon flow. An Integer optimization model was established and solved though two phases. In phase I, the effect mechanism of optimal weights was mined using numerical calculation. In phase Ⅱ, the corresponding empirical regression formulas were respectively provided according to the explored information. Based on the analysis and comparison of aggregation cost between multi-block train with fixed weight and one-block train, applicable operation conditions of the latter was built by adopting the total net savings between the former and the latter as the measure of its comprehensive benefits and tested using discrete event system simulation (DESS) method.
(5) The optimization parameters of multi-block train formation plan. This section studied the parameters for multi-block train formation plan optimization, including the traffic flow in schedule length (planning horizon), accumulation parameters of fixed and non-fixed pattern, two time-saving parameters about block swap that one is relative delay corresponding to passing through and the other is relative saving corresponding to resorting, the up-bound of reclassifi cation and maximum possible blocks in every station.
(6) The optimization model of multi-block train formation plan. Taking the double-block train which has only one block-swap on its route as the research object in this section, under the framework by providing multi-block train and the one-block train services at the same time, the task of wagon flow organization has been reconstructed and the contents and features of the integrated train formation plan (ITFP) have been qualitatively analyzed according to the transitional linkage of blocks between cars and trains. Considering the decomposition of the above task and the actual situation of China, the block-to-train assignment plan (BTAP) was ignored, and then the block network design plan (BNDP), car-to-block assignment plan (CBAP) and train network design plan (TNDP) were taken as0-1decision variables.In order to minimize the total delay cost generated by aggregation, reclassification and transfer in all itineraries, an optimization model of integrated train formation plan (OMITFP) in the situation of giving car routing was constructed while satisfying the limitation in physical aspects, i.e., resorting capacity and reclassification tracks for every terminals, restriction on operational rules and regulations for wagon-flow, i.e., non-separation and consolidation (every railcar originating or re-blocked at a yard and destined for another must travel in a block to the same next re-blocking yard), as well as logical constraints among the decision variables, Considering the model's characteristics, when two0-1decision variables corresponding to block plan and car-to-block assignment were introduced, a model to generating all possible CBAPs was developed with the constraints of non-circuit, maximum frequency of reclassification, cross relation and cutting off unfavorable plans. Besides, the method of scheme tree and backtracking algorithm was designed.
引文
[1]苏顺虎.铁路第六次大面积提速在优化运力资源配置扩大运输能力方面取得的成果[J].铁道经济研究,2007,2：12-19.
[2]赵春雷,刘新,朱家荷.把握提速契机,优化货物列车运输组织[J].铁道货运,2008,3：14-17.
[3]彭其渊,文超,闫海峰.提速战略对我国铁路运输发展的带动作用[J].西南交通大学学报,2008,43(6)：685-691.
[4]郭玉华.我国铁路货运营销发展研究[J].铁道运输与经济,2009,31(1)：1-5.
[5]郭竹学,李学伟.我国高速铁路的理论与实践[J].中国铁路,2010,10：1-3.
[6]安路生.中国高速铁路运输组织[J].中国铁路,2010,12：9-11.
[7]铁道部运输局.全面调整运输生产力布局充分释放铁路运输生产力[J].中国铁路,2006(7)：32-37.
[8]安路生.中国铁路运输新实践[M].北京：中国铁道出版社,2009.
[9]苏顺虎.巩固提速调图成果,深入推进运输集约化,为和谐铁路建设作出积极贡献——在全路货运营销工作会议上的讲话(摘要)[J].中国铁路,2007,9：39-46.
[10]安路生.全面加强调度指挥,大力提高运输效率——在全路调度工作会议上的报告(摘要)[J].中国铁路,2008,2：22-28.
[11]苏顺虎,祝建平,曾卫东.关于西南区域运输组织的调研报告[J].铁道运输与经济,2007,29(1 0)：4-12.
[12]赵春雷.西北区域铁路局新图运输组织工作调研报告[J].铁道运输与经济,2007,29(10)：13-19,41.
[13]郭玉华,祝建平,郭学俊.关于京沪线“一主两翼”新图实施情况的调研[J].铁道运输与经济,2007,29(10)：20-25.
[14]嵇景树,朱光宇,郭峰泉.东北地区运输组织调研报告[J].铁道运输与经济,2007,29(10)：26-38.
[15]陈治亚,凌景文,冯芬玲,等.电力机车跨局运输能耗统计方法及清算办法研究[J].中国铁路,2011,4：41-44.
[16]贾凡.长交路条件下车流组织方法探讨[J].科技广场,2010,4：253-256.
[17]王志美,林柏梁,刘希元.牵引定数不统一的港口后方腹地车流组织优化[J].北京交通大学学报,2011,35(3)：62-67.
[18]赵钢.对开行铁路集装箱中转“五定”班列的思考[J].铁道货运,2005,9：18-21.
[19]郭玉华,何世伟,王保华.铁路中转班列开行方案优化研究[J].铁道学报,2011,33(5)：8-13.
[20]张红亮,杨浩,何世伟.高速铁路背景下我国铁路枢纽编组站发展对策[J].综合运 输,2012,5：58-60.
[21]黎浩东,何世伟,王保华,等.铁路编组站阶段计划编制研究综述[J].铁道学报,2011,33(8)：13-22.
[22]程维生,林柏梁.全路主要编组站改编负荷变化特征分析[J].铁道运输与经济,.2012,34(3)：61-64
[23]Arjang A. Assad. Modelling of rail networks:toward a routing/makeup model [J]. Transportation Research (Part B),1980,14 (1-2):101-114.
[24]Teodor Gabriel Crainic, Gilbert Laporte. Planning models for freight transportation [J]. European Journal of Operational Research,1997,97(3):409-438.
[25]朱松年.列车编组计划优化方法综述[R].成都：西南交通大学,1983.
[26]曹家明.铁路网上车流组织的综合优化[D].成都：西南交通大学,1992.
[27]林柏梁.车流运行径路与列车编组计划的整体优化模型及模拟退火算法[D].成都：西南交通大学,1994.
[28]查伟雄.列车编组计划决策支持系统的研究[D].北京：北方交通大学；1997.
[29]史峰.铁路车流组织优化[D].北京：北方交通大学,1998.
[30]牛惠民.铁路枢纽编组站作业分工整体优化的研究[D].北京：北方交通大学,1999.
[31]闫海峰.结点站间铁路集装箱运输组织的理论与方法研究[D].成都：西南交通大学,2004.
[32]范振平.重载铁路通道车流吸引区域及始发直达重载化研究[D].北京：北京交通大学,2008.
[33]李静.列车编组计划及其模拟退火算法[D].太原：华北工学院,2001.
[34]熊琴.装车地直达列车编组计划近似优化研究[D].北京：北京交通大学,2006.
[35]菅美英.基于MAS的列车编组计划与运行图的一体化优化方法研究[D].北京：北京交通大学,2006.
[36]纪丽君.基于物流成本的装车地车流组织优化方法研究[D].北京：北京交通大学,2008.
[37]乔国会.铁路网车流优化分配模型研究与综合算例分析[D].北京：北京交通大学,2008.
[38]左武.铁路分组货物列车编组计划的遗传算法研究[D].长沙：中南大学,2009.
[39]丁杰.始发直达与重载技术直达列车编组计划匹配技术研究[D].北京：北京交通大学,2010.
[40]李宵寅.基于不确定参数的列车编组计划与车流径路综合优化研究[D].成都：西南交通大学,2011.
[41]长沙铁道学院,北方交通大学.铁路行车组织[M].北京：中国铁道出版社,1982.
[42]郑松富,梁春采.铁路行车组织[M].第二版.北京：中国铁道出版社.1997.
[43]郑时德,吴汉琳.铁路行车组织[M].北京：中国铁道出版社,1988.
[44]曹魁久,孔庆钤.货物列车编组计划[M].北京：中国铁道出版社,1992.
[45]胡思继.铁路行行车组织[M].北京：中国铁道出版社,1998.
[46]吴汉琳,季令.铁路行车组织[M].成都：西南交通大学出版社,1993.
[47]杨浩,何世伟.铁路运输组织学[M].北京：中国铁道出版社,2001.
[48]宋建业,谢金宝.铁路行车组织基础[M].北京：中国铁道出版社,2005.
[49]彭其渊,王慈光.铁路行车组织[M].北京：中国铁道出版社,2007.
[50]曹学明,林柏梁,严贺祥.装车地直达列车开行方案优化模型[J].铁道学报,2006,28(4)：6-11.
[51]曹学明,林柏梁,刘晗,等.基地直达车流组织优化[J].铁道学报,2007,29(1)：16-20.
[52]纪丽君,林柏梁,王志美.基于物流成本的装车地车流组织优化模型研究[J].铁道学报,2009,31(2)：1-6.
[53]强丽霞.基于端点换重的装车地直达车流组织优化模型与算法研究[J].铁道学报,2009,31(6)：91-96.
[54]Yixiang Yue, Leishan Zhou, Qunxing Yue, et al. Multi-route railroad blocking problem by improved model and ant colony algorithm in real world [J]. Computers & Industrial Engineering,2011,60(1):34-42.
[55]林柏梁,朱松年,赵强.技术站直达列车编组计划的阶跃函数模型及同构变换[J].西南交通大学学报,1994,29(1)：91-96.
[56]赵鹏,张进川,唐宝刚.基于组合列车的重载铁路装车区车流组织优化模型研究[J].中国铁道科学,2010,31(6)：116-121.
[57]林柏梁,朱松年,史德耀,等.装车地直达列车编组计划的优化模型[J].中国铁道科学,1995,16(2)：108-114.
[58]张震.单组技术直达列车编组方案最优化的理论和方法[J].铁道运输与经济,1979,1(1)：6-16.
[59]林柏梁,熊天文,朱志国.给定调车线数量条件下的列车编组方案优化[J].中国铁道科学,1993,14(4)：23-31.
[60]林柏梁.具有非线性改编费用的列车编组计划优化[J].铁道学报,1996,18(增刊)：37-42.
[61]林柏梁,朱松年.带有改编能力限制的编组计划优化模型及算法[J].西南交通大学学报,1994,29(5)：488-493.
[62]林柏梁.机车长交路条件下的技术站列车编组计划无调作业参数模型[J].铁道学报,1999,21(6)：6-9.
[63]林柏梁,田亚明,王志美.基于最远站法则的列车编组计划优化双层规划模型[J].中国铁道科学,2011,32(5)：108-113.
[64]范振平,李强.压缩货物运输时间的铁路车流组织双层规划模型[J].物流技术,2012,31(4)：63-67.
[65]Boliang Lin, Zhimei Wang, Lijun Ji, et al. Optimizing the freight train connection service network of a large-scale rail system [J]. Transportation Research (Part B), 2012,46(5):649-667.
[66]林柏梁,朱松年.路网上车流径路与列车编组计划的整体优化[J].铁道学报,1996,18(1)：1-7.
[67]林柏梁.线路与编组站能力限制下的车流组织模型与模拟退火方法[J].北方交通大学学报,1997.,21(3)：264-272.
[68]林柏梁.直达与区段列车编组计划及车流径路的整体优化方法[J].铁道学报,1996,18(5)：1-7.
[69]许红,马建军,龙昭,龙建成,杨浩.技术站单组列车编组方案模型与计算方法的研究[J].铁道学报,,2006,,28(3)：12-18.
[70]H.E波罗沃依.北方交通大学行车组织教研室译.货物运输直达化[M].北京：中国铁道出版社,1981.
[71]朱松年,曹家明,赵强,杜文.车流组织综合优化[J].铁道学报,1993,15(3)：59-69.
[72]史峰,孔庆钤,胡安洲.车流径路与编组计划综合优化的网络方法[J].铁道学报,1997,19(1)：1-6.
[73]纪丽君,林柏梁,乔国会,等.基于多商品流模型的铁路网车流分配和径路优化模型[J].中国铁道科学,,2011,,32(3)：107-110.
[74]林柏梁,陈竹生.路网编组站间重载列车去向优化分布模型[J].铁道学报,1998,20(2)：20-25.
[75]黎浩东,何世伟,宋瑞,等.列车编组计划和技术站布局的综合优化[J].北京交通大学学报,,2010,,34(6)：30-34.
[76]黎浩东,何世伟,王保华,等.铁路车流改编方案与技术站布局的综合优化[J].铁道学报,2011,33(2)：8-12.
[77]田亚明,林柏梁,王志美.考虑始发直达比重的编组站改编能力优化模型[J].交通运输系统工程与信息,2011,11(3)：65-70.
[78]田亚明,杨进.铁路重载运输车流组织与编组站改编能力优化方法研究[J].铁道运输与经济,,2012,,34(9)：49-53.
[79]李夏苗,卢红岩.快运货物列车编组方案的优化[J].中国铁道科学.2004,25(2)：117-120.
[80]梁栋,林柏梁.技术站分组列车编组计划的优化理论及模型研究[J].铁道学报.2006,28(3)：1-5.
[81]梁万荣.铁路枢纽车流组织的研究[J].西南交通大学学报,1995,30(2)：140-144.
[82]徐行方,肖继龙.区段管内车流组织的优化[J].上海铁道大学学报,2000,,21(12)：75-78.
[83]严余松,唐莉,严余伟,等.枢纽小运转列车0-1规划模型及其遗传算法[J].系统工程,2000,,18(6)：67-70.
[84]严余松,朱松年,杜文.枢纽小运转列车运行组织的网络流优化模型[J].西南交通大学学报,2001,36(2)：117-120.
[85]牛惠民,胡安洲.铁路枢纽车流组织的非线性0-1规划模型及算法[J].铁道学报,2001,23(3)：8-12.
[86]史峰,莫辉辉,黎新华,等.区段管内车流组织优化方法[J].交通运输工程学报,2003,3(2)：84-87.
[87]熊天文.列车编组计划车流分析系统[J].铁道运输与经济,2003,25(8)：32-34.
[88]赵升,焦志莹.列车编组计划车流分析系统设计[J].铁道运输与经济,2004,26(7)：57-59.
[89]李莹慧,许红,马建义.计算机编制货物列车编组计划系统设计[J].铁道运输与经济,2005,27(12)：72-74.
[90]程维生,林柏梁.铁路列车编组计划优化系统设计[J].铁道运输与经济,2006,28(2)：70-72.
[91]林柏梁,田亚明,李沛恒,等.技术直达列车开行的车流量下限标准判断系统设计[J].铁路计算机应用,2010,19(6)：22-24.
[92]史峰.直线方向无约束单组列车编组方案的最优化研究[J].铁道学报,1988,10(2)：51-65.
[93]李致中,史峰.铁路网上无约束单组列车编组优化方法[J].铁道学报,1988,10(3)：29-35.
[94]史峰,李致中.路网上技术站多种能力约束下的单组列车编组的网络方法[J].铁道学报,1990,12(4)：51-55.
[95]史峰,李致中,孙焰,等.列车编组计划网络优化方法[J].铁道学报,1994,16(2)：74-79.
[96]雷广萍,袁威.直线方向单组列车编组优化的压缩分枝定界法[J].铁道学报,1989,11(1)：26-38.
[97]李映红,吴世贵,彭其渊.货物列车编组计划网络模型的建立及算法[J].西南交通大学学报,2002,37(1)：68-71.
[98]林柏梁,胡安洲.大规模铁路网上点、线运输能力协调利用的模糊整数规划方法[J].铁道学报,1997,19(4)：9-15.
[99]王保华,何世伟.铁路车流改编方案随机优化模型及其算法[J].中国铁道科学,2009,30(5)：104-108.
[100]Lixing Yang, Ziyou Gao, Keping Li. Railway freight transportation planning with mixed uncertainty of randomness and fuzziness [J]. Applied Soft Computing,2011, 11(1):778-792.
[101]Yiankui Liu, Baoding Liu. Random fuzzy programming with chance measures defined by fuzzy integrals [J].Mathematical and Computer Modelling,2002,36(4-5):509-524.
[102]Baoding Liu. Theory and practice of uncertain programming [M]. Heidelberg: Physica-Verlag,2002.
[103]Baoding Liu. Uncertainty theory:an introduction to its axiomatic Foundations [M]. Berlin:Springer-Verlag,2004.
[104]韩锋.制定列车编组计划的整数规划方法[J].数学实践与认识,1982,2：1-13.
[105]郑时德.制定技术站列车编组计划的0-1规划法[J].铁道学报,1985.7(3)：56-65.
[106]曹家明,朱松年.优化列车编组计划的0-1规划法[J].铁道学报,1992,14(4)：49-57.
[107]林柏梁,朱松年.优化编组计划的非线性0-1规划模型及模拟退火算法[J].铁道学报,1994,16(2)：61-66.
[108]曹家明,朱松年.铁路网上技术直达列车编组计划优化的二次0-1规划法[J].铁道学报,1993,15(2)：65-72.
[109]苗邦均,李致中.技术直达列车最优方案的算法[J].铁道运输与经济,1979,1(1)：139-161.
[110]杨明伦,蔡树英.单组技术直达列车编组计划的树形筛选计算法[J].铁道运输与经济,1981,3(3)：42-47.
[111]吴汉琳.装车地直达列车组织方案的研究[J].铁道学报,1987,9(4)：17-26.
[112]何邦模,刘春煌,张理丹.选择单组技术直达列车编组计划的分析计算法[J].铁道学报,1990,14(3)：28-38.
[113]黄民,薛秀懿,刘卫华.始发直达列车开行方案的优化[J].中国铁道科学,1990,,11(2)99-106.
[114]查伟雄,黄孝章,秦作睿.直线方向列车编组计划有利去向模型及其多项式算法[J].北方交通大学学报,1996.20(6)：641-644.
[115]查伟雄,秦作睿.路网上列车编组计划的有利去向模型及其网络流算法[J].铁道学报,1998,20(2)：12-19.
[116]闫海峰,彭其渊,殷勇.集装箱班列编组计划优化模型研究[J].铁道学报,,2003,25(5)：14-18.
[117]杨时刚,史峰,李致中.制定列车编组计划的人工神经网络方法[J].长沙铁道学院学报,2002,20(3)：79-84.
[118]Jean Francois Cordeau, Paolo Toth, Daniele Vigo. A survey of optimization models for train routing and scheduling [J]. Transportation Science,1998,32(4):380-404.
[119]Ali E. Haghani. A combined model of train routing, makeup, and empty car distribution [D]. Illinois:Northwestern University,1986.
[120]Harry N. Newton. Network design under budget constraints with application to the railroad blocking problem [D]. Alabama:Auburn University,1997.
[121]E.R. Kraft, A reservation-based railway network operations management system [D]. Philadelphia:University of Pennsylvania,1998.
[122]Jian Liu.Solving real-life transportation scheduling problems [D]. Florida:University of Florida,2003.
[123]Krishna C. Jha.Very large-scale neighborhood search heuristics for combinatorial optimization problems [D]. Florida:University of Florida,2004.
[124]Sahin G. New Combinatorial approaches for solving railroad planning and scheduling problems [D]. Florida:University of Florida,2006.
[125]Yungcheng Lai. Increasing railway efficiency and capacity through improved operations, control and planning [D]. Illinois:University of Illinois at Urbana-Champaign,2008
[126]Laura Galli. Combinatorial and robust optimization models and algorithms for railway applications [D]. Bologna:University di Bologna,2009.
[127]Ashish Kumar Nemani. Combinatorial approaches to solve scheduling problems [D]. Florida:University of Florida,2009.
[128]Lawrence D. Bodin, Bruce L. Golden, Allan D. Schuster, William Romig. A Model for the blocking of trains [J]. Transportation Research (Part B),1980,14 (1-2):115-120.
[129]Arjang A. Assad. Analysis of rail classification policies [J]. INFORMS,1983,21(4): 293-314.
[130]Newton H.N., Barnhart C., Vance P.H. Constructing railroad blocking plans to minimize handling costs [J]. Transportation Science,1998,32 (4):330-345.
[131]Barnhart C., Jin H., Vance P. H. Railroad blocking:a network design application [J]. Operational Research,2000,48(4):603-614.
[132]Ravindra K. Ahuja, Krishna C. Jha, Jian Liu. Solving real-life railroad blocking problems [J]. Interfaces,2007,37(5):404-419.
[133]Masoud Yaghini, Amir Foroughi, Behnam Nadjari. Solving railroad blocking problem using ant colony optimization algorithm [J].Applied Mathematical Modelling,2011, 35(12):5579-5591.
[134]Teodor Gabriel Crainic, Jacques A. Ferland, Jean Marc Rousseau. A tactical planning model for rail freight transportation [J]. Transportation Science,1984,18(2):165-184.
[135]Ali E. Haghani. Formulation and solution of a combined train routing and makeup, and empty car distribution model [J]. Transportation Research (Part B),1989.23(6): 433-452.
[136]Mark H. Keaton. Designing optimal railroad operating plans:Lagrangian relaxation and heuristic approaches [J]. Transportation Research (Part B),1989,23(6):415-431.
[137]Mark H. Keaton. Designing railroad operating plans:a dual adjustment method for implementing lagrangian relaxation [J]. Transportation Science,1992,26(4):263-279.
[138]David R. Martinelli, Hualiang Teng. Optimization of railway operations using neural networks [J]. Transportation Research (Part C),1996,4(1):33-49.
[139]Angel Marin, Javier Salmeron. Tactical design of rail freight networks part Ⅰ:exact and heuristic methods [J].European Journal of Operational Research,1996,90(1): 26-44.
[140]Angel Marin, Javier Salmeron.Tactical design of rail freight networks part Ⅱ:local search methods with statistical analysis [J].European Journal of Operational Research, 1996,94(1):43-53.
[141]Krishna C. Jha, Ravindra K. Ahuja, Guveng Sahin. New approaches for solving the block-to-train assignment problem [J]. Networks,2008,51(1):48-62.
[142]Masoud Yaghini, Mohsen Momeni, Mohammadreza Sarmadi.An improved local branching approach for train formation planning [J].Applied Mathematical Modelling, 2012, Forthcoming.
[143]Masoud Yaghini, Mohsen Momeni, Mohammadreza Sarmadi.Solving train formation problem using simulated annealing algorithm in a simplex framework [J].Journal of Advanced Transportation,2012, Forthcoming.
[144]Huntley C. L., Brown D. E., Sappington D. E., et al. Freight routing and scheduling at CSX transportation [J]. Interfaces,1995,25(3):58-71.
[145]Michael Francis Gorman. An application of genetic and tabu searches to the freight railroad operating plan problem [J]. Annals of Operations Research,1998,78:51-69.
[146]T. Godwin, Ram Gopalan, T. T. Narendran. Freight train routing and scheduling in a passenger rail network:computational complexity and the stepwise dispatching heuristic [J]. Asia-Pacific Journal of Operational Research,2007,24(4):499-533.
[147]M.A. Shafia, S. J. Sadjadi, A. Jamili. Robust train formation planning [J]. Proceedings of the Institution of Mechanical Engineers (Part F),2010,224(2):75-90.
[148]刑文训,谢金星.现代优化计算方法[M].第二版.北京：清华大学出版社,,2005.
[149]谢金星.网络优化[M].北京：清华大学出版社,.2009.
[150]Raf Jans. Classification of Dantzig-Wolfe reformulations for binary mixed integer programming problems [J].European Journal of Operational Research,2010,204(2): 251-254.
[151]Francois Vanderbeck, Martin W.P. Savelsbergh. A generic view of Dantzig-Wolfe decomposition in mixed integer programming [J].Operations Research Letters,2006, 34(3):296-306.
[152]朱道立.大系统优化理论和应用[M].上海：上海交通大学出版社,1987.
[153]Ravindra K. Ahuja, Magnanti Thomas. L., Orlin James. B. Network flows:theory algorithms and applications [M].New Jerse:Prentice Hall,1993.
[154]AM Geoffrion. Generalized benders decomposition [J]. Journal of Optimization Theory and Applications.1972,10(4):237-260.
[155]Alysson M.Costa.A survey on benders decomposition applied to fixed-charge network design problems [J].Computers & Operations Research,2005,32(6):1429-1450.
[156]毛保华,张国伍.编组站出发策略系统分析[J].铁道学报,1987,9(2)：38-44.
[157]李夏苗,邹毅峰,胡思继.技术站列车出发策略的经济性分析[J].中国铁道科学,,2001,22(2)：79-87.
[158]Ravindra K. Ahuja, Claudio B. Cunha, Guveng Sahin. Network models in railroad planning and scheduling [J].Tutorials in Operations Research,2005:54-101.
[159]Phil Ireland, Rod Case, John Fallis, et al. The Canadian Pacific Railway transforms operations research by using models to develop its operating plans [J]. Interfaces, 2004,34(1):5-14.
[160]Natale Papola, Francesco Filippi, Guido Gentile, et al. Schedule-based modeling of transportation networks theory and applications [M]. In:Nigel H. M. Wilson, Agostino Nuzzolo. Schedule-based modeling of transportation networks:theory and applications. Berlin:Springer,2009.
[161]Nemani A. K., Ravindra K. Ahuja. OR models in freight railroad industry [M]. Wiley Encyclopedia of Operations Research and Management Science,2011.
[162]Bussieck MR, Winter T, Zimmermann UT. Discrete optimization in public rail transport [J]. Mathematical Programming,1997,79(1-3):415-444.
[163]Ouorou A., Mahey P., Vial J. Ph. A survey of algorithms for convex multicommodity flow problems [J]. Management Science,2000,46(1):126-147.
[164]Gorman M.F., Crook K., Sellers D. North American freight rail industry real-time optimized equipment distribution systems:State of the practice [J]. Transportation Research (Part C),2011,19(1):103-114.
[165]戴时.铁路货运组织[M].北京：中国铁道出版社,2008.
[166]成都铁路局.成都铁路局货物列车编组计划[Z].成都：成都铁路局,.2010.
[167]王慈光.运输模型及优化.第二版[M].成都：西南交通大学出版社,2010.
[168]彭其渊,闫海峰,魏德勇.集装箱班列运输组织[M].成都：四川科学技术出版社.,2005.
[169]胡思继.列车运行组织及通过能力理论[M].北京：中国铁道出版社,1993.
[170]李海鹰,杨肇夏.中国铁路货物运输组织模式改革研究[C]//可持续发展的中国交通——2005全国博士生学术论坛(交通运输工程学科)论文集(上册).中国北京,2005-07-01, 2005：240-244.
[171]史峰,李致中.集结耗费的计算公式[J].铁道学报,1992,14(4)：35-41.
[172]何世伟,宋瑞,杨浩,等.列车提速对编组站运营工作的影响研究[J].铁道学报,2000,22(5)：6-12.
[173]朱晓立,李夏苗.提速干线上编组站到解系统匹配与协调关系的研究[J].中国铁道科学,2004,25(4)：112-115.
[174]朱晓立,李夏苗.提速干线编组站出发子系统内部匹配与协调关系[J].中国铁道科学,2006,27(5)：118-121.
[175]刘旭,谭立刚,杨浩,等.编组站通过能力和改编能力模拟计算系统的研究[J].铁道学报,2002,24(5)：11-15.
[176]邓永录,梁之舜.随机点过程及其应用[M].北京：科学出版社,1992.
[177]王慈光.描述简单货车集结过程的群论模型[J].铁道学报,1994,16(3)：66-77.
[178]何平.数理统计与多元统计[M].成都：西南交通大学出版社,2007.
[179]Richard A. Groeneveld, Glen Meeden. Measuring skewness and kurtosis [J].The Statistician,1984,33(4):391-399.
[180]闫海峰.铁路结点站集装箱班列集结过程[J].西南交通大学学报,2006.41(5)：620-625.
[181]燕娟,郎茂祥,朱亮.我国铁路双层集装箱列车运输组织研究[J].铁道货运,,2006,,6：39-41.
[182]王如义,王慈光,景云,等.定点发车模式中最小编成辆数的研究[J].铁道运输与经济,2009,31(8)：85-88.
[183]王如义,王慈光.编组站自编始发货物列车定点发车模式的适应性研究[J].北京工业大学学报,,2011,,37(4)：541-546.
[184]崔园园.编组站货车集结仿真研究[D].北京：北京交通大学,2011.
[185]刘晨,孙晚华.放宽条件定点集结模式下编组站车列解体顺序优化研究[J].铁道学报,,2012,34(2)：1-7.
[186]汪波,魏玉光,杨浩,等.编组站货物列车定点集结模式下发车时刻表的编制[J].中国铁道科学,2008,29(2)：126-130.
[187]王国玉,韩调,刘春煌.固定车底循环直达运输有利性的探讨[J].铁道运输与经济,1982,4(4)：1-7.
[188]王如义,王慈光,张展杰,等.编组站始发欠轴货物列车在途中补轴的有利性研究[J].中国铁路,2009,5：47-50.
[189]韦有双,杨湘龙,王飞.虚拟现实技术与系统仿真[M].北京：国防工业出版社,2004.
[190]刘勇,王德才,冯正超.离散事件系统仿真建模与仿真策略[J].西南师范大学学报(自然科学版),2005,.30(6)：1019-1025.
[191]牛惠民.车流波动条件下铁路枢纽编组站作业分工的优化[J].铁道学报,.2003,25(1)：3-8.
[192]张伯敏.灰色模型与铁路车流预测控制[J].上海交通大学学报,2000,34(增刊)：49-51.
[193]查伟雄,熊桂林.铁路旅客OD矩阵推算模型及算法设计[J].系统工程,2004,22(12)：82-86.
[194]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社.2002.
[195]刘思峰,党耀国,方志耕.灰色系统理论及其应用[M].北京：科学出版社,2004.
[196]张于心,张国伍,毛保华.提高哈尔滨枢纽综合能力的网络系统分析[J].铁道科技动态,1988,11：17-20.
[197]张于心.提高哈尔滨铁路枢纽综合能力的研究[J].铁道运输与经济,1989,,12(3)：23-25.
[198]王慈光.货车集结参数的研究[J].铁道运输与经济.,2006,2,8(7)：83-85.
[199]何邦模,王国玉,陈元龙等.关于车辆集结参数的研究[J].铁道运输与经济,1992,14(4)：59-66.
[200]李夏苗.货车无改编通过技术站节省时间的研究[J].长沙铁道学院学报,1993,11(2)：36-40.
[201]钱名军.技术站列车编组计划要素计算方法的商榷[J].兰州交通大学学报,2007,26(3)：48-50.
[202]谢金宝,韩瑛,王兴芳,等.货车无改编通过技术站节省时间的计算[J].兰州交通大学学报,2012,31(1)：1-4.
[203]Endong Zhu, Teodor Gabriel Crainic, M. Gendreau. Integrated service network design in rail freight transportation [R]. Canada, Montreal:CIRRELT,2009,45.
[204]王伟,刘军,李海鹰.具有时效性的铁路快捷货物运输服务设计[J].物流技术.2009,28(2)：103-105.
[205]刘其斌,马桂贞.铁路车站及枢纽[M].北京：中国铁道出版社,1999.
[206]陈伯羽.铁路编组场线路固定使用方案优选方法研究[J].铁道科学与工程学报,2006,3(6)：80-82.
[207]孙玉明,李红璇.编制编组站调车场线路固定使用方案的探讨[J].铁道运输与经济,2003,25(12)：20-21.
[208]M. Minoux. Network synthesis and optimum network design problems:models, solution methods and applications [J]. Networks,19:313-360,1989.
[209]Teodor Gabriel Crainic. Service network design in freight transportation [J]. European Journal of Operational Research,2000,122(2):272-288.
[210]Nicole Wieberneit. Service network design for freight transportation:a review [J]. OR Spectrum,2008,30(l):77-112.
[211]沈睿.铁路行包快运服务网络设计理论与方法研究[D].北京：北京交通大学,2006.
[212]杨希流.关于铁路列车编组计划优化问题的探讨[J].铁道运输与经济,2009,31(10)：11-13.
[213]林柏梁,彭辉,任保国.铁路网上带权重的车流径路优化方法[J].北方交通大学学报.1996,20(6)：645-650.
[214]林柏梁,朱松年,陈竹生等.路网上车流径路优化的0-1规划模型及其合理径路集生成集法[J].铁道学报.1997,19(1)：7-12.
[215]王保华,何世伟,宋瑞,等.综合运输体系下快捷货运网络流量分配优化模型及算法[J].铁道学报.2009,31(2)：12-16.
[216]王保华,何世伟,宋瑞,等.快捷货运动态服务网络设计优化模型及其算法[J].铁道学报.2009,31(5)：17-22.
[217]刘澜,许乃星.紧急状态下铁路旅客运输径路优化[J].中国铁道科学,2010,31(4)：125-130.
[218]Sible Alumur, Bahar Y. Kara. Network hub location problems:the state of the art [J]. European Journal of Operational Research,2008,190(1):1-21.
[219]林柏梁,徐忠义,黄民,等.编组站布局规划模型[J].铁道学报,2002,24(3)：5-8.
[220]史峰,方琪根,黎新华,等.技术站布局的双层规划优化方法[J].铁道学报,2003,25(2)：1-4.
[2]赵春雷,刘新,朱家荷.把握提速契机,优化货物列车运输组织[J].铁道货运,2008,3：14-17.
[3]彭其渊,文超,闫海峰.提速战略对我国铁路运输发展的带动作用[J].西南交通大学学报,2008,43(6)：685-691.
[4]郭玉华.我国铁路货运营销发展研究[J].铁道运输与经济,2009,31(1)：1-5.
[5]郭竹学,李学伟.我国高速铁路的理论与实践[J].中国铁路,2010,10：1-3.
[6]安路生.中国高速铁路运输组织[J].中国铁路,2010,12：9-11.
[7]铁道部运输局.全面调整运输生产力布局充分释放铁路运输生产力[J].中国铁路,2006(7)：32-37.
[8]安路生.中国铁路运输新实践[M].北京：中国铁道出版社,2009.
[9]苏顺虎.巩固提速调图成果,深入推进运输集约化,为和谐铁路建设作出积极贡献——在全路货运营销工作会议上的讲话(摘要)[J].中国铁路,2007,9：39-46.
[10]安路生.全面加强调度指挥,大力提高运输效率——在全路调度工作会议上的报告(摘要)[J].中国铁路,2008,2：22-28.
[11]苏顺虎,祝建平,曾卫东.关于西南区域运输组织的调研报告[J].铁道运输与经济,2007,29(1 0)：4-12.
[12]赵春雷.西北区域铁路局新图运输组织工作调研报告[J].铁道运输与经济,2007,29(10)：13-19,41.
[13]郭玉华,祝建平,郭学俊.关于京沪线“一主两翼”新图实施情况的调研[J].铁道运输与经济,2007,29(10)：20-25.
[14]嵇景树,朱光宇,郭峰泉.东北地区运输组织调研报告[J].铁道运输与经济,2007,29(10)：26-38.
[15]陈治亚,凌景文,冯芬玲,等.电力机车跨局运输能耗统计方法及清算办法研究[J].中国铁路,2011,4：41-44.
[16]贾凡.长交路条件下车流组织方法探讨[J].科技广场,2010,4：253-256.
[17]王志美,林柏梁,刘希元.牵引定数不统一的港口后方腹地车流组织优化[J].北京交通大学学报,2011,35(3)：62-67.
[18]赵钢.对开行铁路集装箱中转“五定”班列的思考[J].铁道货运,2005,9：18-21.
[19]郭玉华,何世伟,王保华.铁路中转班列开行方案优化研究[J].铁道学报,2011,33(5)：8-13.
[20]张红亮,杨浩,何世伟.高速铁路背景下我国铁路枢纽编组站发展对策[J].综合运 输,2012,5：58-60.
[21]黎浩东,何世伟,王保华,等.铁路编组站阶段计划编制研究综述[J].铁道学报,2011,33(8)：13-22.
[22]程维生,林柏梁.全路主要编组站改编负荷变化特征分析[J].铁道运输与经济,.2012,34(3)：61-64
[23]Arjang A. Assad. Modelling of rail networks:toward a routing/makeup model [J]. Transportation Research (Part B),1980,14 (1-2):101-114.
[24]Teodor Gabriel Crainic, Gilbert Laporte. Planning models for freight transportation [J]. European Journal of Operational Research,1997,97(3):409-438.
[25]朱松年.列车编组计划优化方法综述[R].成都：西南交通大学,1983.
[26]曹家明.铁路网上车流组织的综合优化[D].成都：西南交通大学,1992.
[27]林柏梁.车流运行径路与列车编组计划的整体优化模型及模拟退火算法[D].成都：西南交通大学,1994.
[28]查伟雄.列车编组计划决策支持系统的研究[D].北京：北方交通大学；1997.
[29]史峰.铁路车流组织优化[D].北京：北方交通大学,1998.
[30]牛惠民.铁路枢纽编组站作业分工整体优化的研究[D].北京：北方交通大学,1999.
[31]闫海峰.结点站间铁路集装箱运输组织的理论与方法研究[D].成都：西南交通大学,2004.
[32]范振平.重载铁路通道车流吸引区域及始发直达重载化研究[D].北京：北京交通大学,2008.
[33]李静.列车编组计划及其模拟退火算法[D].太原：华北工学院,2001.
[34]熊琴.装车地直达列车编组计划近似优化研究[D].北京：北京交通大学,2006.
[35]菅美英.基于MAS的列车编组计划与运行图的一体化优化方法研究[D].北京：北京交通大学,2006.
[36]纪丽君.基于物流成本的装车地车流组织优化方法研究[D].北京：北京交通大学,2008.
[37]乔国会.铁路网车流优化分配模型研究与综合算例分析[D].北京：北京交通大学,2008.
[38]左武.铁路分组货物列车编组计划的遗传算法研究[D].长沙：中南大学,2009.
[39]丁杰.始发直达与重载技术直达列车编组计划匹配技术研究[D].北京：北京交通大学,2010.
[40]李宵寅.基于不确定参数的列车编组计划与车流径路综合优化研究[D].成都：西南交通大学,2011.
[41]长沙铁道学院,北方交通大学.铁路行车组织[M].北京：中国铁道出版社,1982.
[42]郑松富,梁春采.铁路行车组织[M].第二版.北京：中国铁道出版社.1997.
[43]郑时德,吴汉琳.铁路行车组织[M].北京：中国铁道出版社,1988.
[44]曹魁久,孔庆钤.货物列车编组计划[M].北京：中国铁道出版社,1992.
[45]胡思继.铁路行行车组织[M].北京：中国铁道出版社,1998.
[46]吴汉琳,季令.铁路行车组织[M].成都：西南交通大学出版社,1993.
[47]杨浩,何世伟.铁路运输组织学[M].北京：中国铁道出版社,2001.
[48]宋建业,谢金宝.铁路行车组织基础[M].北京：中国铁道出版社,2005.
[49]彭其渊,王慈光.铁路行车组织[M].北京：中国铁道出版社,2007.
[50]曹学明,林柏梁,严贺祥.装车地直达列车开行方案优化模型[J].铁道学报,2006,28(4)：6-11.
[51]曹学明,林柏梁,刘晗,等.基地直达车流组织优化[J].铁道学报,2007,29(1)：16-20.
[52]纪丽君,林柏梁,王志美.基于物流成本的装车地车流组织优化模型研究[J].铁道学报,2009,31(2)：1-6.
[53]强丽霞.基于端点换重的装车地直达车流组织优化模型与算法研究[J].铁道学报,2009,31(6)：91-96.
[54]Yixiang Yue, Leishan Zhou, Qunxing Yue, et al. Multi-route railroad blocking problem by improved model and ant colony algorithm in real world [J]. Computers & Industrial Engineering,2011,60(1):34-42.
[55]林柏梁,朱松年,赵强.技术站直达列车编组计划的阶跃函数模型及同构变换[J].西南交通大学学报,1994,29(1)：91-96.
[56]赵鹏,张进川,唐宝刚.基于组合列车的重载铁路装车区车流组织优化模型研究[J].中国铁道科学,2010,31(6)：116-121.
[57]林柏梁,朱松年,史德耀,等.装车地直达列车编组计划的优化模型[J].中国铁道科学,1995,16(2)：108-114.
[58]张震.单组技术直达列车编组方案最优化的理论和方法[J].铁道运输与经济,1979,1(1)：6-16.
[59]林柏梁,熊天文,朱志国.给定调车线数量条件下的列车编组方案优化[J].中国铁道科学,1993,14(4)：23-31.
[60]林柏梁.具有非线性改编费用的列车编组计划优化[J].铁道学报,1996,18(增刊)：37-42.
[61]林柏梁,朱松年.带有改编能力限制的编组计划优化模型及算法[J].西南交通大学学报,1994,29(5)：488-493.
[62]林柏梁.机车长交路条件下的技术站列车编组计划无调作业参数模型[J].铁道学报,1999,21(6)：6-9.
[63]林柏梁,田亚明,王志美.基于最远站法则的列车编组计划优化双层规划模型[J].中国铁道科学,2011,32(5)：108-113.
[64]范振平,李强.压缩货物运输时间的铁路车流组织双层规划模型[J].物流技术,2012,31(4)：63-67.
[65]Boliang Lin, Zhimei Wang, Lijun Ji, et al. Optimizing the freight train connection service network of a large-scale rail system [J]. Transportation Research (Part B), 2012,46(5):649-667.
[66]林柏梁,朱松年.路网上车流径路与列车编组计划的整体优化[J].铁道学报,1996,18(1)：1-7.
[67]林柏梁.线路与编组站能力限制下的车流组织模型与模拟退火方法[J].北方交通大学学报,1997.,21(3)：264-272.
[68]林柏梁.直达与区段列车编组计划及车流径路的整体优化方法[J].铁道学报,1996,18(5)：1-7.
[69]许红,马建军,龙昭,龙建成,杨浩.技术站单组列车编组方案模型与计算方法的研究[J].铁道学报,,2006,,28(3)：12-18.
[70]H.E波罗沃依.北方交通大学行车组织教研室译.货物运输直达化[M].北京：中国铁道出版社,1981.
[71]朱松年,曹家明,赵强,杜文.车流组织综合优化[J].铁道学报,1993,15(3)：59-69.
[72]史峰,孔庆钤,胡安洲.车流径路与编组计划综合优化的网络方法[J].铁道学报,1997,19(1)：1-6.
[73]纪丽君,林柏梁,乔国会,等.基于多商品流模型的铁路网车流分配和径路优化模型[J].中国铁道科学,,2011,,32(3)：107-110.
[74]林柏梁,陈竹生.路网编组站间重载列车去向优化分布模型[J].铁道学报,1998,20(2)：20-25.
[75]黎浩东,何世伟,宋瑞,等.列车编组计划和技术站布局的综合优化[J].北京交通大学学报,,2010,,34(6)：30-34.
[76]黎浩东,何世伟,王保华,等.铁路车流改编方案与技术站布局的综合优化[J].铁道学报,2011,33(2)：8-12.
[77]田亚明,林柏梁,王志美.考虑始发直达比重的编组站改编能力优化模型[J].交通运输系统工程与信息,2011,11(3)：65-70.
[78]田亚明,杨进.铁路重载运输车流组织与编组站改编能力优化方法研究[J].铁道运输与经济,,2012,,34(9)：49-53.
[79]李夏苗,卢红岩.快运货物列车编组方案的优化[J].中国铁道科学.2004,25(2)：117-120.
[80]梁栋,林柏梁.技术站分组列车编组计划的优化理论及模型研究[J].铁道学报.2006,28(3)：1-5.
[81]梁万荣.铁路枢纽车流组织的研究[J].西南交通大学学报,1995,30(2)：140-144.
[82]徐行方,肖继龙.区段管内车流组织的优化[J].上海铁道大学学报,2000,,21(12)：75-78.
[83]严余松,唐莉,严余伟,等.枢纽小运转列车0-1规划模型及其遗传算法[J].系统工程,2000,,18(6)：67-70.
[84]严余松,朱松年,杜文.枢纽小运转列车运行组织的网络流优化模型[J].西南交通大学学报,2001,36(2)：117-120.
[85]牛惠民,胡安洲.铁路枢纽车流组织的非线性0-1规划模型及算法[J].铁道学报,2001,23(3)：8-12.
[86]史峰,莫辉辉,黎新华,等.区段管内车流组织优化方法[J].交通运输工程学报,2003,3(2)：84-87.
[87]熊天文.列车编组计划车流分析系统[J].铁道运输与经济,2003,25(8)：32-34.
[88]赵升,焦志莹.列车编组计划车流分析系统设计[J].铁道运输与经济,2004,26(7)：57-59.
[89]李莹慧,许红,马建义.计算机编制货物列车编组计划系统设计[J].铁道运输与经济,2005,27(12)：72-74.
[90]程维生,林柏梁.铁路列车编组计划优化系统设计[J].铁道运输与经济,2006,28(2)：70-72.
[91]林柏梁,田亚明,李沛恒,等.技术直达列车开行的车流量下限标准判断系统设计[J].铁路计算机应用,2010,19(6)：22-24.
[92]史峰.直线方向无约束单组列车编组方案的最优化研究[J].铁道学报,1988,10(2)：51-65.
[93]李致中,史峰.铁路网上无约束单组列车编组优化方法[J].铁道学报,1988,10(3)：29-35.
[94]史峰,李致中.路网上技术站多种能力约束下的单组列车编组的网络方法[J].铁道学报,1990,12(4)：51-55.
[95]史峰,李致中,孙焰,等.列车编组计划网络优化方法[J].铁道学报,1994,16(2)：74-79.
[96]雷广萍,袁威.直线方向单组列车编组优化的压缩分枝定界法[J].铁道学报,1989,11(1)：26-38.
[97]李映红,吴世贵,彭其渊.货物列车编组计划网络模型的建立及算法[J].西南交通大学学报,2002,37(1)：68-71.
[98]林柏梁,胡安洲.大规模铁路网上点、线运输能力协调利用的模糊整数规划方法[J].铁道学报,1997,19(4)：9-15.
[99]王保华,何世伟.铁路车流改编方案随机优化模型及其算法[J].中国铁道科学,2009,30(5)：104-108.
[100]Lixing Yang, Ziyou Gao, Keping Li. Railway freight transportation planning with mixed uncertainty of randomness and fuzziness [J]. Applied Soft Computing,2011, 11(1):778-792.
[101]Yiankui Liu, Baoding Liu. Random fuzzy programming with chance measures defined by fuzzy integrals [J].Mathematical and Computer Modelling,2002,36(4-5):509-524.
[102]Baoding Liu. Theory and practice of uncertain programming [M]. Heidelberg: Physica-Verlag,2002.
[103]Baoding Liu. Uncertainty theory:an introduction to its axiomatic Foundations [M]. Berlin:Springer-Verlag,2004.
[104]韩锋.制定列车编组计划的整数规划方法[J].数学实践与认识,1982,2：1-13.
[105]郑时德.制定技术站列车编组计划的0-1规划法[J].铁道学报,1985.7(3)：56-65.
[106]曹家明,朱松年.优化列车编组计划的0-1规划法[J].铁道学报,1992,14(4)：49-57.
[107]林柏梁,朱松年.优化编组计划的非线性0-1规划模型及模拟退火算法[J].铁道学报,1994,16(2)：61-66.
[108]曹家明,朱松年.铁路网上技术直达列车编组计划优化的二次0-1规划法[J].铁道学报,1993,15(2)：65-72.
[109]苗邦均,李致中.技术直达列车最优方案的算法[J].铁道运输与经济,1979,1(1)：139-161.
[110]杨明伦,蔡树英.单组技术直达列车编组计划的树形筛选计算法[J].铁道运输与经济,1981,3(3)：42-47.
[111]吴汉琳.装车地直达列车组织方案的研究[J].铁道学报,1987,9(4)：17-26.
[112]何邦模,刘春煌,张理丹.选择单组技术直达列车编组计划的分析计算法[J].铁道学报,1990,14(3)：28-38.
[113]黄民,薛秀懿,刘卫华.始发直达列车开行方案的优化[J].中国铁道科学,1990,,11(2)99-106.
[114]查伟雄,黄孝章,秦作睿.直线方向列车编组计划有利去向模型及其多项式算法[J].北方交通大学学报,1996.20(6)：641-644.
[115]查伟雄,秦作睿.路网上列车编组计划的有利去向模型及其网络流算法[J].铁道学报,1998,20(2)：12-19.
[116]闫海峰,彭其渊,殷勇.集装箱班列编组计划优化模型研究[J].铁道学报,,2003,25(5)：14-18.
[117]杨时刚,史峰,李致中.制定列车编组计划的人工神经网络方法[J].长沙铁道学院学报,2002,20(3)：79-84.
[118]Jean Francois Cordeau, Paolo Toth, Daniele Vigo. A survey of optimization models for train routing and scheduling [J]. Transportation Science,1998,32(4):380-404.
[119]Ali E. Haghani. A combined model of train routing, makeup, and empty car distribution [D]. Illinois:Northwestern University,1986.
[120]Harry N. Newton. Network design under budget constraints with application to the railroad blocking problem [D]. Alabama:Auburn University,1997.
[121]E.R. Kraft, A reservation-based railway network operations management system [D]. Philadelphia:University of Pennsylvania,1998.
[122]Jian Liu.Solving real-life transportation scheduling problems [D]. Florida:University of Florida,2003.
[123]Krishna C. Jha.Very large-scale neighborhood search heuristics for combinatorial optimization problems [D]. Florida:University of Florida,2004.
[124]Sahin G. New Combinatorial approaches for solving railroad planning and scheduling problems [D]. Florida:University of Florida,2006.
[125]Yungcheng Lai. Increasing railway efficiency and capacity through improved operations, control and planning [D]. Illinois:University of Illinois at Urbana-Champaign,2008
[126]Laura Galli. Combinatorial and robust optimization models and algorithms for railway applications [D]. Bologna:University di Bologna,2009.
[127]Ashish Kumar Nemani. Combinatorial approaches to solve scheduling problems [D]. Florida:University of Florida,2009.
[128]Lawrence D. Bodin, Bruce L. Golden, Allan D. Schuster, William Romig. A Model for the blocking of trains [J]. Transportation Research (Part B),1980,14 (1-2):115-120.
[129]Arjang A. Assad. Analysis of rail classification policies [J]. INFORMS,1983,21(4): 293-314.
[130]Newton H.N., Barnhart C., Vance P.H. Constructing railroad blocking plans to minimize handling costs [J]. Transportation Science,1998,32 (4):330-345.
[131]Barnhart C., Jin H., Vance P. H. Railroad blocking:a network design application [J]. Operational Research,2000,48(4):603-614.
[132]Ravindra K. Ahuja, Krishna C. Jha, Jian Liu. Solving real-life railroad blocking problems [J]. Interfaces,2007,37(5):404-419.
[133]Masoud Yaghini, Amir Foroughi, Behnam Nadjari. Solving railroad blocking problem using ant colony optimization algorithm [J].Applied Mathematical Modelling,2011, 35(12):5579-5591.
[134]Teodor Gabriel Crainic, Jacques A. Ferland, Jean Marc Rousseau. A tactical planning model for rail freight transportation [J]. Transportation Science,1984,18(2):165-184.
[135]Ali E. Haghani. Formulation and solution of a combined train routing and makeup, and empty car distribution model [J]. Transportation Research (Part B),1989.23(6): 433-452.
[136]Mark H. Keaton. Designing optimal railroad operating plans:Lagrangian relaxation and heuristic approaches [J]. Transportation Research (Part B),1989,23(6):415-431.
[137]Mark H. Keaton. Designing railroad operating plans:a dual adjustment method for implementing lagrangian relaxation [J]. Transportation Science,1992,26(4):263-279.
[138]David R. Martinelli, Hualiang Teng. Optimization of railway operations using neural networks [J]. Transportation Research (Part C),1996,4(1):33-49.
[139]Angel Marin, Javier Salmeron. Tactical design of rail freight networks part Ⅰ:exact and heuristic methods [J].European Journal of Operational Research,1996,90(1): 26-44.
[140]Angel Marin, Javier Salmeron.Tactical design of rail freight networks part Ⅱ:local search methods with statistical analysis [J].European Journal of Operational Research, 1996,94(1):43-53.
[141]Krishna C. Jha, Ravindra K. Ahuja, Guveng Sahin. New approaches for solving the block-to-train assignment problem [J]. Networks,2008,51(1):48-62.
[142]Masoud Yaghini, Mohsen Momeni, Mohammadreza Sarmadi.An improved local branching approach for train formation planning [J].Applied Mathematical Modelling, 2012, Forthcoming.
[143]Masoud Yaghini, Mohsen Momeni, Mohammadreza Sarmadi.Solving train formation problem using simulated annealing algorithm in a simplex framework [J].Journal of Advanced Transportation,2012, Forthcoming.
[144]Huntley C. L., Brown D. E., Sappington D. E., et al. Freight routing and scheduling at CSX transportation [J]. Interfaces,1995,25(3):58-71.
[145]Michael Francis Gorman. An application of genetic and tabu searches to the freight railroad operating plan problem [J]. Annals of Operations Research,1998,78:51-69.
[146]T. Godwin, Ram Gopalan, T. T. Narendran. Freight train routing and scheduling in a passenger rail network:computational complexity and the stepwise dispatching heuristic [J]. Asia-Pacific Journal of Operational Research,2007,24(4):499-533.
[147]M.A. Shafia, S. J. Sadjadi, A. Jamili. Robust train formation planning [J]. Proceedings of the Institution of Mechanical Engineers (Part F),2010,224(2):75-90.
[148]刑文训,谢金星.现代优化计算方法[M].第二版.北京：清华大学出版社,,2005.
[149]谢金星.网络优化[M].北京：清华大学出版社,.2009.
[150]Raf Jans. Classification of Dantzig-Wolfe reformulations for binary mixed integer programming problems [J].European Journal of Operational Research,2010,204(2): 251-254.
[151]Francois Vanderbeck, Martin W.P. Savelsbergh. A generic view of Dantzig-Wolfe decomposition in mixed integer programming [J].Operations Research Letters,2006, 34(3):296-306.
[152]朱道立.大系统优化理论和应用[M].上海：上海交通大学出版社,1987.
[153]Ravindra K. Ahuja, Magnanti Thomas. L., Orlin James. B. Network flows:theory algorithms and applications [M].New Jerse:Prentice Hall,1993.
[154]AM Geoffrion. Generalized benders decomposition [J]. Journal of Optimization Theory and Applications.1972,10(4):237-260.
[155]Alysson M.Costa.A survey on benders decomposition applied to fixed-charge network design problems [J].Computers & Operations Research,2005,32(6):1429-1450.
[156]毛保华,张国伍.编组站出发策略系统分析[J].铁道学报,1987,9(2)：38-44.
[157]李夏苗,邹毅峰,胡思继.技术站列车出发策略的经济性分析[J].中国铁道科学,,2001,22(2)：79-87.
[158]Ravindra K. Ahuja, Claudio B. Cunha, Guveng Sahin. Network models in railroad planning and scheduling [J].Tutorials in Operations Research,2005:54-101.
[159]Phil Ireland, Rod Case, John Fallis, et al. The Canadian Pacific Railway transforms operations research by using models to develop its operating plans [J]. Interfaces, 2004,34(1):5-14.
[160]Natale Papola, Francesco Filippi, Guido Gentile, et al. Schedule-based modeling of transportation networks theory and applications [M]. In:Nigel H. M. Wilson, Agostino Nuzzolo. Schedule-based modeling of transportation networks:theory and applications. Berlin:Springer,2009.
[161]Nemani A. K., Ravindra K. Ahuja. OR models in freight railroad industry [M]. Wiley Encyclopedia of Operations Research and Management Science,2011.
[162]Bussieck MR, Winter T, Zimmermann UT. Discrete optimization in public rail transport [J]. Mathematical Programming,1997,79(1-3):415-444.
[163]Ouorou A., Mahey P., Vial J. Ph. A survey of algorithms for convex multicommodity flow problems [J]. Management Science,2000,46(1):126-147.
[164]Gorman M.F., Crook K., Sellers D. North American freight rail industry real-time optimized equipment distribution systems:State of the practice [J]. Transportation Research (Part C),2011,19(1):103-114.
[165]戴时.铁路货运组织[M].北京：中国铁道出版社,2008.
[166]成都铁路局.成都铁路局货物列车编组计划[Z].成都：成都铁路局,.2010.
[167]王慈光.运输模型及优化.第二版[M].成都：西南交通大学出版社,2010.
[168]彭其渊,闫海峰,魏德勇.集装箱班列运输组织[M].成都：四川科学技术出版社.,2005.
[169]胡思继.列车运行组织及通过能力理论[M].北京：中国铁道出版社,1993.
[170]李海鹰,杨肇夏.中国铁路货物运输组织模式改革研究[C]//可持续发展的中国交通——2005全国博士生学术论坛(交通运输工程学科)论文集(上册).中国北京,2005-07-01, 2005：240-244.
[171]史峰,李致中.集结耗费的计算公式[J].铁道学报,1992,14(4)：35-41.
[172]何世伟,宋瑞,杨浩,等.列车提速对编组站运营工作的影响研究[J].铁道学报,2000,22(5)：6-12.
[173]朱晓立,李夏苗.提速干线上编组站到解系统匹配与协调关系的研究[J].中国铁道科学,2004,25(4)：112-115.
[174]朱晓立,李夏苗.提速干线编组站出发子系统内部匹配与协调关系[J].中国铁道科学,2006,27(5)：118-121.
[175]刘旭,谭立刚,杨浩,等.编组站通过能力和改编能力模拟计算系统的研究[J].铁道学报,2002,24(5)：11-15.
[176]邓永录,梁之舜.随机点过程及其应用[M].北京：科学出版社,1992.
[177]王慈光.描述简单货车集结过程的群论模型[J].铁道学报,1994,16(3)：66-77.
[178]何平.数理统计与多元统计[M].成都：西南交通大学出版社,2007.
[179]Richard A. Groeneveld, Glen Meeden. Measuring skewness and kurtosis [J].The Statistician,1984,33(4):391-399.
[180]闫海峰.铁路结点站集装箱班列集结过程[J].西南交通大学学报,2006.41(5)：620-625.
[181]燕娟,郎茂祥,朱亮.我国铁路双层集装箱列车运输组织研究[J].铁道货运,,2006,,6：39-41.
[182]王如义,王慈光,景云,等.定点发车模式中最小编成辆数的研究[J].铁道运输与经济,2009,31(8)：85-88.
[183]王如义,王慈光.编组站自编始发货物列车定点发车模式的适应性研究[J].北京工业大学学报,,2011,,37(4)：541-546.
[184]崔园园.编组站货车集结仿真研究[D].北京：北京交通大学,2011.
[185]刘晨,孙晚华.放宽条件定点集结模式下编组站车列解体顺序优化研究[J].铁道学报,,2012,34(2)：1-7.
[186]汪波,魏玉光,杨浩,等.编组站货物列车定点集结模式下发车时刻表的编制[J].中国铁道科学,2008,29(2)：126-130.
[187]王国玉,韩调,刘春煌.固定车底循环直达运输有利性的探讨[J].铁道运输与经济,1982,4(4)：1-7.
[188]王如义,王慈光,张展杰,等.编组站始发欠轴货物列车在途中补轴的有利性研究[J].中国铁路,2009,5：47-50.
[189]韦有双,杨湘龙,王飞.虚拟现实技术与系统仿真[M].北京：国防工业出版社,2004.
[190]刘勇,王德才,冯正超.离散事件系统仿真建模与仿真策略[J].西南师范大学学报(自然科学版),2005,.30(6)：1019-1025.
[191]牛惠民.车流波动条件下铁路枢纽编组站作业分工的优化[J].铁道学报,.2003,25(1)：3-8.
[192]张伯敏.灰色模型与铁路车流预测控制[J].上海交通大学学报,2000,34(增刊)：49-51.
[193]查伟雄,熊桂林.铁路旅客OD矩阵推算模型及算法设计[J].系统工程,2004,22(12)：82-86.
[194]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社.2002.
[195]刘思峰,党耀国,方志耕.灰色系统理论及其应用[M].北京：科学出版社,2004.
[196]张于心,张国伍,毛保华.提高哈尔滨枢纽综合能力的网络系统分析[J].铁道科技动态,1988,11：17-20.
[197]张于心.提高哈尔滨铁路枢纽综合能力的研究[J].铁道运输与经济,1989,,12(3)：23-25.
[198]王慈光.货车集结参数的研究[J].铁道运输与经济.,2006,2,8(7)：83-85.
[199]何邦模,王国玉,陈元龙等.关于车辆集结参数的研究[J].铁道运输与经济,1992,14(4)：59-66.
[200]李夏苗.货车无改编通过技术站节省时间的研究[J].长沙铁道学院学报,1993,11(2)：36-40.
[201]钱名军.技术站列车编组计划要素计算方法的商榷[J].兰州交通大学学报,2007,26(3)：48-50.
[202]谢金宝,韩瑛,王兴芳,等.货车无改编通过技术站节省时间的计算[J].兰州交通大学学报,2012,31(1)：1-4.
[203]Endong Zhu, Teodor Gabriel Crainic, M. Gendreau. Integrated service network design in rail freight transportation [R]. Canada, Montreal:CIRRELT,2009,45.
[204]王伟,刘军,李海鹰.具有时效性的铁路快捷货物运输服务设计[J].物流技术.2009,28(2)：103-105.
[205]刘其斌,马桂贞.铁路车站及枢纽[M].北京：中国铁道出版社,1999.
[206]陈伯羽.铁路编组场线路固定使用方案优选方法研究[J].铁道科学与工程学报,2006,3(6)：80-82.
[207]孙玉明,李红璇.编制编组站调车场线路固定使用方案的探讨[J].铁道运输与经济,2003,25(12)：20-21.
[208]M. Minoux. Network synthesis and optimum network design problems:models, solution methods and applications [J]. Networks,19:313-360,1989.
[209]Teodor Gabriel Crainic. Service network design in freight transportation [J]. European Journal of Operational Research,2000,122(2):272-288.
[210]Nicole Wieberneit. Service network design for freight transportation:a review [J]. OR Spectrum,2008,30(l):77-112.
[211]沈睿.铁路行包快运服务网络设计理论与方法研究[D].北京：北京交通大学,2006.
[212]杨希流.关于铁路列车编组计划优化问题的探讨[J].铁道运输与经济,2009,31(10)：11-13.
[213]林柏梁,彭辉,任保国.铁路网上带权重的车流径路优化方法[J].北方交通大学学报.1996,20(6)：645-650.
[214]林柏梁,朱松年,陈竹生等.路网上车流径路优化的0-1规划模型及其合理径路集生成集法[J].铁道学报.1997,19(1)：7-12.
[215]王保华,何世伟,宋瑞,等.综合运输体系下快捷货运网络流量分配优化模型及算法[J].铁道学报.2009,31(2)：12-16.
[216]王保华,何世伟,宋瑞,等.快捷货运动态服务网络设计优化模型及其算法[J].铁道学报.2009,31(5)：17-22.
[217]刘澜,许乃星.紧急状态下铁路旅客运输径路优化[J].中国铁道科学,2010,31(4)：125-130.
[218]Sible Alumur, Bahar Y. Kara. Network hub location problems:the state of the art [J]. European Journal of Operational Research,2008,190(1):1-21.
[219]林柏梁,徐忠义,黄民,等.编组站布局规划模型[J].铁道学报,2002,24(3)：5-8.
[220]史峰,方琪根,黎新华,等.技术站布局的双层规划优化方法[J].铁道学报,2003,25(2)：1-4.