大型有色冶炼企业铁路运输智能优化调度方法及应用
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摘要
铁路运输是大型有色冶炼企业的大动脉,它肩负着运送物资保障生产的重任,在有色冶炼企业物流环节中具有十分重要的地位。但随着国民经济的发展,有色冶炼企业生产规模日益扩大,物料的运输量迅猛增加,而我国有色冶炼企业铁路规模偏小,铁路布局、调度方式与企业生产方式密切相关,具有各站场分布分散、调车以小运转作业为主、铁路线路短、自备车与租用车混合编组等特点,并存在检斤作业滞留罚金等问题,使得企业铁路运输调度作业问题较复杂、难度大。目前,我国有色冶炼企业铁路大多以人工调度为主,使得货运站长期处于满负荷运行状态,容易导致列车运行效率低,物料运输不及时问题,甚至出现堵车、安全事故等现象,严重制约了有色冶炼企业发展。因此,针对大型有色冶炼企业货运铁路特点,研究有色冶炼企业货运站列车的编组与调度方法,对缩短车辆周转时间,避免滞留罚金问题,提高我国有色冶炼企业的铁路货运组织作业效率具有重要现实意义。
论文在分析研究大型有色冶炼企业铁路运输作业特点的基础上,依据企业铁路运输编解作业、取送车作业及调车计划编制需求,建立了铁路运输调度模型,并研究了基于蚁群交互式优化算法的铁路调度优化方法,提出了企业铁路调车计划编制优化方法和不同布局的企业铁路取送车作业优化方法,成功应用于企业铁路运输智能调度系统中。论文主要研究工作及创新性成果包括：
(1)针对有色冶炼企业铁路运输网调度过程复杂,情况多变且影响因素多的问题,分析研究了运输调度过程的特点及作业流程,将复杂的铁路运输调度模型分解为铁路编解模型、树枝型铁路取送车作业模型及混合型铁路取送车作业模型,降低了调度模型的复杂度,提高了企业铁路运输调度模型的普适性。
(2)为了大型企业铁路运输调度模型实时求解需要,针对经典遗传算法的局限性,提出了一种蚁群交互式优化算法。该算法将蚁群与遗传算法融入文化算法框架,组成基于蚁群的主群体空间和信念空间两大空间,主群体空间在进化过程中定期组织最差个体向信念空间提供的种群最优模式学习,从而充分利用了优秀个体所包含的特征信息,避免了蚁群算法种群单一性的问题,在很大程度上提高了算法的收敛速度。
(3)针对企业铁路的调度作业计划人工制定准确率低、负担重、作业连贯性差等问题,根据大型企业铁路运输各作业子系统具有前后串联、相互影响的特点,把复杂的调度问题分解为列车分组、列车解编组、列车进路安排、列车取送车作业四个子问题,将优化方法分别用于铁路调度作业中的几个不同的子问题,避免了复杂优化计算问题,极大地提高了模型优化求解的效率。同时,运用遗传精英蚁群算法对协调优化模型进行求解,优化了有色冶炼企业某段时刻内的配流,缩短了列车在站停留时间。针对企业租用国家铁路列车延时罚款问题,将罚款因素作为约束条件加入优化问题中,设计了白适应的惩罚函数,并将其与遗传精英蚁群算法相结合,解决了带有惩罚时间约束的铁路调度问题,有效地避免了企业铁路运输租用列车的罚款问题。
(4)根据企业编组站树枝型专用线的特点,利用图论的知识把编组站装卸货专用线分布抽象为汉密尔顿图,将树枝型取送车问题转化为旅行商(TSP)问题,采用了一种新的融合算法——遗传蚁群算法对该问题进行求解,经过遗传算法的初步搜索并生成初始信息素分布,增强了蚁群算法的正反馈机制,降低了蚁群算法中的参数调整程度。此外,遗传算法与蚁群算法结合后,在算法的收敛速度加快的同时,蚁群算法中的α、β、p参数对取送车问题规模变化的敏感度降低,提高了算法的鲁棒性。在蚁群算法阶段使用最大-最小蚂蚁系统(MMAS),而且同时采用信息素的局部更新和全局更新规则,有效避免了陷入局部最优问题。
(5)设计开发了大型有色冶炼企业铁路运输智能调度系统,实现了企业铁路调度过程的钩机划、调车作业在线优化与离线仿真功能,在保证安全和生产需求的条件下,提高了调车效率,有效了加速车辆的周转,降低企业运输成本。
Railway transportation is the main artery of large-scale non-ferrous metal smelting enterprises in our country. It plays a very important role in the logistics chain, as it carries out the key task of transporting materials and guaranteeing regular production. With the development of national economy, the rapid increase in the production scale of non-ferrous metal smelting enterprises, the amount of material transportation is also rapidly increasing. However, the railways related to non-ferrous metal smelting enterprises in our country are quite in small scales. The distribution of railway lines and the mode of dispatching are closely related to the production mode of an enterprise. It adds to the difficulty and the complexity of the enterprise railway transportation to have the problems of dispersed distribution of railway stations, small-scaled dispatching mode, short lines of railways, mixed marshalling of self-owned and rented carriages and the fines caused by the cargo retention. At present, the railways owned by non-ferrous metal smelting enterprises in our country are mainly manually scheduled, making freight stations running at full capacity most of the time, which easily leads to the problems such as low efficiency of train operation, not timely arrival of cargoes or even traffic jams and accidents, and seriously hampers the development of non-ferrous smelting business. Therefore, it is of great significance to do some research on the mode of marshaling and dispatching of cargo trains of large non-ferrous smelting business, based on its characteristics, in order to shorten the turnaround time of vehicles, avoid fines caused by cargo detention and improve the efficiency of our non-ferrous smelting enterprises organizing operation.
The thesis sets up a scheduling model of railway transportation based on the analysis of the characteristics of large enterprises rail transportation operation, according to the operation of train sorting and classifying, pickup and delivery, and the demand of dispatching organization of railway transportation. The thesis also studies railway scheduling optimization based on cultural evolution algorithm. Thus the thesis proposes railway shunting planning optimization methods, and placing-in and taking-out vehicle optimization of different layout of the enterprise railways, and applies them successfully to enterprise rail transportation intelligent scheduling systems. The main research and innovative achievements of the thesis are as follow:
(1) According to the complex mechanism of business scheduling process for railway transportation network, changing circumstances and the impact of many factors, the characteristics of railway transport scheduling process, transportation scheduling processes are analyzed and studied in order to break up the complex railway scheduling model into sorting and classifying model, tree branch lines model and comprehensive layout train placing-in and taking-out model to reduce the complexity of scheduling model, lay a solid foundation for the model optimization solution. In addition, the actual situation of rail transport lines to improve enterprise rail transport scheduling model universality has been fully taken into consideration in the comprehensive layout train placing-in and taking-out model.
(2) Ant colony interactive optimization algorithm is put forward to meet the demand of timely solution to the scheduling model of large enterprise railway transportation, making up for the limitations of classical genetic algorithm. The ant colony algorithm and genetic algorithm are blended into the cultural framework, form the main group space and belief space based on ant colony groups. In the evolution process, the main groups periodically organize the worst individuals to learn the best mode population provided by the belief space, taking full advantage of the excellent characteristics of the information contained in the individual, avoiding the population singularity problem of ant colony algorithm, thus greatly improving the convergence speed.
(3) The complex train scheduling process is divided into four sub processes:train formation optimization, sorting and marshalling operation optimization, train route arrangement and operation of fetching and delivering carriages, applying the optimization method to several different sub problems in the train scheduling operation to avoid the complexity of optimization problems and greatly improve the efficiency of solution with the optimization model, considering such problems as low accuracy, heavy burden and poor continuity in manual operation of making railway scheduling plan and the characteristics of backward and forward interrelationship and inter influence of the operation subsystems of large enterprise railway transportation. In addition, it can optimize the process of distribution of materials of the metallurgical enterprises within a certain period of time and shorten the period of time when trains are kept in the train station to apply the hybrid elite ant colony algorithm to coordinate and optimize models. In order to solve the problem of train delay caused by renting the national railway system, the penalty factor is included in the optimization as constraint condition. An adaptive penalty function is designed and combined with the genetic elite ant colony algorithm, which solves the problem of train scheduling with the restriction of time penalty and effectively avoids penalty caused by rented train of enterprise railway transportation.
(4) A new blending method is applied, which is called a genetic ant colony algorithm, to solve the problem of transformation of the branch-type pickup and delivery vehicles problem into traveling salesman problem (TSP), according to the characteristics of branch-type-specific line in the company marshalling yard, using graph theory knowledge to abstract the distribution for loading and unloading cargo in marshalling yard into the map of Hamilton. This mode genetic algorithm initially searches the information and generates the initial pheromone distribution, enhances the positive and negative feedback mechanism of ant colony algorithm, and greatly reduces the degree of parameter adjustment of ant colony algorithm. In addition, the ant colony algorithm α, β, p parameters reduce sensitivity to changes of the pickup and delivery vehicles in problem size and improve the robustness of the algorithm after the combination of genetic algorithms and ant colony algorithm, increasing the speed of the convergence of the algorithm. It can avoid falling into local optimization problem to a certain extent to apply the maximum-the smallest ant system (MMAS), during the period of having the ant colony algorithm and to use the local pheromone update and global update rules at the same time.
(5) Rail transport intelligent scheduling system for large non-ferrous smelting enterprises is designed and developed, which ensures machine drawing in the process of enterprise rail scheduling, on line optimization of dispatching operation and the off-line simulation function. The system increases shunting efficiency, meanwhile, accelerates vehicle turnover effectively and reduces transportation costs of enterprises with the conditions of ensuring the safety and meeting production demand.
论文在分析研究大型有色冶炼企业铁路运输作业特点的基础上,依据企业铁路运输编解作业、取送车作业及调车计划编制需求,建立了铁路运输调度模型,并研究了基于蚁群交互式优化算法的铁路调度优化方法,提出了企业铁路调车计划编制优化方法和不同布局的企业铁路取送车作业优化方法,成功应用于企业铁路运输智能调度系统中。论文主要研究工作及创新性成果包括：
(1)针对有色冶炼企业铁路运输网调度过程复杂,情况多变且影响因素多的问题,分析研究了运输调度过程的特点及作业流程,将复杂的铁路运输调度模型分解为铁路编解模型、树枝型铁路取送车作业模型及混合型铁路取送车作业模型,降低了调度模型的复杂度,提高了企业铁路运输调度模型的普适性。
(2)为了大型企业铁路运输调度模型实时求解需要,针对经典遗传算法的局限性,提出了一种蚁群交互式优化算法。该算法将蚁群与遗传算法融入文化算法框架,组成基于蚁群的主群体空间和信念空间两大空间,主群体空间在进化过程中定期组织最差个体向信念空间提供的种群最优模式学习,从而充分利用了优秀个体所包含的特征信息,避免了蚁群算法种群单一性的问题,在很大程度上提高了算法的收敛速度。
(3)针对企业铁路的调度作业计划人工制定准确率低、负担重、作业连贯性差等问题,根据大型企业铁路运输各作业子系统具有前后串联、相互影响的特点,把复杂的调度问题分解为列车分组、列车解编组、列车进路安排、列车取送车作业四个子问题,将优化方法分别用于铁路调度作业中的几个不同的子问题,避免了复杂优化计算问题,极大地提高了模型优化求解的效率。同时,运用遗传精英蚁群算法对协调优化模型进行求解,优化了有色冶炼企业某段时刻内的配流,缩短了列车在站停留时间。针对企业租用国家铁路列车延时罚款问题,将罚款因素作为约束条件加入优化问题中,设计了白适应的惩罚函数,并将其与遗传精英蚁群算法相结合,解决了带有惩罚时间约束的铁路调度问题,有效地避免了企业铁路运输租用列车的罚款问题。
(4)根据企业编组站树枝型专用线的特点,利用图论的知识把编组站装卸货专用线分布抽象为汉密尔顿图,将树枝型取送车问题转化为旅行商(TSP)问题,采用了一种新的融合算法——遗传蚁群算法对该问题进行求解,经过遗传算法的初步搜索并生成初始信息素分布,增强了蚁群算法的正反馈机制,降低了蚁群算法中的参数调整程度。此外,遗传算法与蚁群算法结合后,在算法的收敛速度加快的同时,蚁群算法中的α、β、p参数对取送车问题规模变化的敏感度降低,提高了算法的鲁棒性。在蚁群算法阶段使用最大-最小蚂蚁系统(MMAS),而且同时采用信息素的局部更新和全局更新规则,有效避免了陷入局部最优问题。
(5)设计开发了大型有色冶炼企业铁路运输智能调度系统,实现了企业铁路调度过程的钩机划、调车作业在线优化与离线仿真功能,在保证安全和生产需求的条件下,提高了调车效率,有效了加速车辆的周转,降低企业运输成本。
Railway transportation is the main artery of large-scale non-ferrous metal smelting enterprises in our country. It plays a very important role in the logistics chain, as it carries out the key task of transporting materials and guaranteeing regular production. With the development of national economy, the rapid increase in the production scale of non-ferrous metal smelting enterprises, the amount of material transportation is also rapidly increasing. However, the railways related to non-ferrous metal smelting enterprises in our country are quite in small scales. The distribution of railway lines and the mode of dispatching are closely related to the production mode of an enterprise. It adds to the difficulty and the complexity of the enterprise railway transportation to have the problems of dispersed distribution of railway stations, small-scaled dispatching mode, short lines of railways, mixed marshalling of self-owned and rented carriages and the fines caused by the cargo retention. At present, the railways owned by non-ferrous metal smelting enterprises in our country are mainly manually scheduled, making freight stations running at full capacity most of the time, which easily leads to the problems such as low efficiency of train operation, not timely arrival of cargoes or even traffic jams and accidents, and seriously hampers the development of non-ferrous smelting business. Therefore, it is of great significance to do some research on the mode of marshaling and dispatching of cargo trains of large non-ferrous smelting business, based on its characteristics, in order to shorten the turnaround time of vehicles, avoid fines caused by cargo detention and improve the efficiency of our non-ferrous smelting enterprises organizing operation.
The thesis sets up a scheduling model of railway transportation based on the analysis of the characteristics of large enterprises rail transportation operation, according to the operation of train sorting and classifying, pickup and delivery, and the demand of dispatching organization of railway transportation. The thesis also studies railway scheduling optimization based on cultural evolution algorithm. Thus the thesis proposes railway shunting planning optimization methods, and placing-in and taking-out vehicle optimization of different layout of the enterprise railways, and applies them successfully to enterprise rail transportation intelligent scheduling systems. The main research and innovative achievements of the thesis are as follow:
(1) According to the complex mechanism of business scheduling process for railway transportation network, changing circumstances and the impact of many factors, the characteristics of railway transport scheduling process, transportation scheduling processes are analyzed and studied in order to break up the complex railway scheduling model into sorting and classifying model, tree branch lines model and comprehensive layout train placing-in and taking-out model to reduce the complexity of scheduling model, lay a solid foundation for the model optimization solution. In addition, the actual situation of rail transport lines to improve enterprise rail transport scheduling model universality has been fully taken into consideration in the comprehensive layout train placing-in and taking-out model.
(2) Ant colony interactive optimization algorithm is put forward to meet the demand of timely solution to the scheduling model of large enterprise railway transportation, making up for the limitations of classical genetic algorithm. The ant colony algorithm and genetic algorithm are blended into the cultural framework, form the main group space and belief space based on ant colony groups. In the evolution process, the main groups periodically organize the worst individuals to learn the best mode population provided by the belief space, taking full advantage of the excellent characteristics of the information contained in the individual, avoiding the population singularity problem of ant colony algorithm, thus greatly improving the convergence speed.
(3) The complex train scheduling process is divided into four sub processes:train formation optimization, sorting and marshalling operation optimization, train route arrangement and operation of fetching and delivering carriages, applying the optimization method to several different sub problems in the train scheduling operation to avoid the complexity of optimization problems and greatly improve the efficiency of solution with the optimization model, considering such problems as low accuracy, heavy burden and poor continuity in manual operation of making railway scheduling plan and the characteristics of backward and forward interrelationship and inter influence of the operation subsystems of large enterprise railway transportation. In addition, it can optimize the process of distribution of materials of the metallurgical enterprises within a certain period of time and shorten the period of time when trains are kept in the train station to apply the hybrid elite ant colony algorithm to coordinate and optimize models. In order to solve the problem of train delay caused by renting the national railway system, the penalty factor is included in the optimization as constraint condition. An adaptive penalty function is designed and combined with the genetic elite ant colony algorithm, which solves the problem of train scheduling with the restriction of time penalty and effectively avoids penalty caused by rented train of enterprise railway transportation.
(4) A new blending method is applied, which is called a genetic ant colony algorithm, to solve the problem of transformation of the branch-type pickup and delivery vehicles problem into traveling salesman problem (TSP), according to the characteristics of branch-type-specific line in the company marshalling yard, using graph theory knowledge to abstract the distribution for loading and unloading cargo in marshalling yard into the map of Hamilton. This mode genetic algorithm initially searches the information and generates the initial pheromone distribution, enhances the positive and negative feedback mechanism of ant colony algorithm, and greatly reduces the degree of parameter adjustment of ant colony algorithm. In addition, the ant colony algorithm α, β, p parameters reduce sensitivity to changes of the pickup and delivery vehicles in problem size and improve the robustness of the algorithm after the combination of genetic algorithms and ant colony algorithm, increasing the speed of the convergence of the algorithm. It can avoid falling into local optimization problem to a certain extent to apply the maximum-the smallest ant system (MMAS), during the period of having the ant colony algorithm and to use the local pheromone update and global update rules at the same time.
(5) Rail transport intelligent scheduling system for large non-ferrous smelting enterprises is designed and developed, which ensures machine drawing in the process of enterprise rail scheduling, on line optimization of dispatching operation and the off-line simulation function. The system increases shunting efficiency, meanwhile, accelerates vehicle turnover effectively and reduces transportation costs of enterprises with the conditions of ensuring the safety and meeting production demand.
引文
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