基于不确定理论和混合智能算法的调车机车运用研究

铁路车站作业计划编制过程中,调机运用计划是调度指挥的关键。根据列车占用调机时间段的原则和作业环境随机因素的情况,对待作业车列占用调机的时间进行区段式划分,得到进行作业的偏差期望模型,结合概率论及不确定理论,采用混合智能算法对其进行求解,并利用计算机找到待作业车列的作业顺序。     

基于统筹法的调车机车运用计划编制方法研究

分析技术站的技术作业内容及各作业工序的统筹关系,在明确本班列车到达计划信息、装卸排空任务、出发列车的要求,以及各作业时间标准的情况下,根据各作业工序之间的关系及工序的可移性原理,安排区段站调车机车解体和编组作业计划,再结合轮廓的装卸计划、排空计划和出发车流需要等,确定调车机车的具体取送车时刻和内容,以完善车站调机运用计划的编制。     

车辆调度问题的混合算法

针对多配送中心车辆调度问题,以及考虑到遗传算法与禁忌搜索算法各自的优缺点建立了多配送中心车辆优化调度的数学模型设计了遗传算法和禁忌搜索算法相结合的混合算法。该算法避免了遗传算法和禁忌搜索算法各自的缺陷,并进行了仿真分析。     

铁路枢纽双编组站静态配流协同优化研究

枢纽内跨编组站进行协同配流有助于优化枢纽内的车流接续,提高全局配流质量。引入跨编组站协同配流思想,构建铁路枢纽双编组站静态配流协同优化模型,以最小化双编组站的配流总代价、最大化双编组站的总满轴列车数为优化目标,考虑车流量约束、列车到解编发作业与枢纽小运转列车走行的接续时间约束、出发列车满轴与不违编约束、调机资源使用约束。以列车等级、编组去向数及出发时刻排序作为配流代价。设计理想点算法将多目标转化为单目标进行求解,最后运用算例对模型及算法的有效性进行验证。结果表明,双编组站联合配流相较于两编组站单独配流,可使满轴列车总数增加1列、站存车总数减少13辆、配流总代价降低5.4%,从而达到更佳的配流效果。     

成网条件下高速铁路动车组运用计划优化模型研究

通过阐述高速铁路成网后对动车组运用计划的影响,分析成网条件下的动车组运用计划,分步骤构建时空网络,以路径费用最小为目标函数,综合考虑动车组运用计划和检修计划等相关约束,建立动车组运用计划优化模型,选用大规模邻域搜索算法进行模型求解。以京沪、沪宁高速铁路数据为例,验证该算法的可行性和有效性。比较大规模邻域搜索算法与模拟退火算法的优化结果,认为大规模邻域搜索算法能够较快地获取优化结果。     

多层次高速铁路列车开行方案评价及优化方法研究

在综合考虑铁路运输企业和旅客效益的基础上,从经济效益、技术、列车服务能力和旅客服务效果4个角度着手,构建单列车、动车组交路和列车开行整体方案的多层次评价指标体系。运用带权重的主成分分析法,根据动车组交路和单列车评价结果确定调整对象。在调整对象确定后,进一步分析各调整对象存在的主要问题,运用传统的主成分分析法明确调整方向,结合整体方案评价结果提出具体调整措施。最后以京沪高速铁路2014年暑运列车开行方案为例,验证该评价方法的有效性和可行性。     

动车组运用接续网络优化方法研究

通过分析,将动车组接续周转计划的优化求解过程归结为在动车组运用接续网络上应用搜索算法寻找满足约束条件和使目标函数最优的路径集合的过程。优化动车组运用接续网络可有效提高求解效率。应用匈牙利算法设计了以"使用动车组最少"为目标的动车组运用最优接续网络生成方法,以京津城际铁路本线列车平日运行图为例对方法进行验证。结果表明,该方法可求解最优接续网络,运算效率较高。     

基于混合禁忌搜索算法的物流配送路径优化问题研究

在对配送路径优化问题进行描述的基础上,建立物流配送路径优化问题的数学模型,提出了一种求解车辆路径问题的混合禁忌搜索算法。在该混合算法中,通过车辆—任务分配结构的划分,将大规模问题拆分成可并行计算的若干小规模问题,减少了算法的计算时间。并通过理论分析和仿真算例,证明了该混合禁忌搜索算法的有效性。     

铁路编组站车流配流问题研究

在论述铁路编组站车流作业过程的基础上,确定配流的基本思路和约束条件,以及配流的原则,将车辆在编组站总停留时间最少定为配流问题的基本目标,提出配流的具体方法,采取从始发列车约束条件反推计算解体列车解体顺序,求得车流接续时间最短的最优解.并以重庆西站为例,将配流运算系统在 office Excel 2003的环境下投入应用.     

柳州南站驼峰解体能力优化研究

针对柳南站驼峰解体能力制约整体作业能力的现状,从系统内部作业组织出发,分析影响驼峰解体能力的主要原因,并从系统协调的角度对驼峰与调机能力协调、调机作业分工协调、解体作业环节协调和换班整备组织协调等方面进行分析研究,提出强化调机分工、协调作业妨碍、调整作业环节、优化交接整备和加强联劳协作的具体措施,有效提高了车站驼峰解体能力.     

Scheduling technicians for planned maintenance of geographically distributed equipment

A real-world planned maintenance scheduling problem that exists at several business units within United Technologies Corporation (UTC) is addressed in this paper. The scheduling problem is formulated as a multiple tour maximum collection problem with time-dependent rewards and an adaptive memory tabu search heuristic is developed to solve it. The effectiveness of the proposed solution approach is examined using real-world problem instances supplied by UTC. Relevant upper bounds are derived for the application. Results of numerical experiments indicate that the proposed tabu search heuristic is able to obtain near optimal solutions for large-size (i.e., actual) problem instances in reasonable computation time.     

An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem

This paper studies the heterogeneous fixed fleet vehicle routing problem (HFFVRP), in which the fleet is composed of a fixed number of vehicles with different capacities, fixed costs, and variable costs. Given the fleet composition, the HFFVRP is to determine a vehicle scheduling strategy with the objective of minimizing the total transportation cost. We propose a multistart adaptive memory programming (MAMP) and path relinking algorithm to solve this problem. Through the search memory, MAMP at each iteration constructs multiple provisional solutions, which are further improved by a modified tabu search. As an intensification strategy, path relinking is integrated to enhance the performance of MAMP. We conduct a series of experiments to evaluate and demonstrate the effectiveness of the proposed algorithm.     

货物列车编组计划与编组站负荷分工联合优化模型

提出编组站解编负荷状态评价函数及曲线,以车流组织费用最小化和编组站解编负荷状态最佳化为目标,构建货物列车编组计划与编组站负荷分工联合优化模型,并考虑编组方案惟一性、解编能力和调车线数量等约束条件。利用目标规划法(GP)将模型由多目标转化为单目标,设计了相应的搜索生成方法并编程予以实现。最后基于实际路网案例,使用LINGO数学软件完成模型的求解和对比分析,验证了模型的合理性和有效性。     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

Optimization of postal express line network under mixed driving pattern of trucks

Details about the movement of trucks on postal express lines are investigated to improve the performances of mail distribution. A mixed driving pattern of trucks is introduced to minimize the transportation cost of a postal express line network with a service level requirement. We formulate this problem as a mixed p meeting depots location with shipment scheduling problem and build a MINLP model. A two-level tabu search procedure based on shipment grouping method is developed. Through a series of computational experiments and sensitivity analysis on different instances, some managerial insights of the network under mixed driving pattern are revealed.     

A reactive tabu search algorithm for the multi-depot container truck transportation problem

A container truck transportation problem that involves multiple depots with time windows at both origins and destinations, including the reposition of empty containers, is formulated as a multi-traveling salesman problem with time windows (m-TSPTW) with multiple depots. Since the problem is NP-hard, a cluster method and a reactive tabu search (RTS) algorithm are developed to solve the problem. The two methods are compared with the mixed integer program which can be used to find optimum solutions for small size problems. The computational results show that the developed methods, particularly the RTS algorithm, can be efficiently used to solve the problem.     

Demand-responsive transit circulator service network design

Commuter rail systems are being introduced into many urban areas as an alternative mode to automobiles for commuting trips. It is anticipated that the shift from the auto mode to rail mode can greatly help alleviate traffic congestion in urban road networks. However, the right-of-way of many existing commuter rail systems is usually not ideally located. Since the locations of rail systems were typically chosen long ago to serve the needs of freight customers, the majority of current commuter rail passengers have to take a non-walkable connecting trip to reach their final destinations after departing the most conveniently located rail stations. To make rail a more viable commuting option and thus more competitive to the auto mode, a bus feeder or circulator system is proposed for transporting passengers from their departing rail stations to final work destinations in a seamless transfer manner. The key research question with operating such a bus circulator system is how to optimally determine a bus route and stopping sequence for each circulating tour by using the real-time demand information. In this paper, we name this joint routing and stopping optimization problem the circulator service network design problem, the objective of which is to minimize the total tour cost incurred to bus passengers and operators with respect to minimizing the walk time of each individual bus passenger. A bi-level nonlinear mixed integer programming model is constructed and a tabu search method with different local search strategies and neighborhood evaluation methods is then developed for tackling the circulator service network design problem.     

基于列生成的高速铁路车站作业计划调度调整方法研究

大型高速铁路车站到发线及列车进路的冲突是调度调整的重点,为了提高调整效率和效果,在将问题抽象为时空网络上多商品流的基础上,考虑技术作业要求、作业间隔时间、线路和进路运用规则等约束条件,以列车总晚点时间小、旅客便捷性好为目标,构建集到发线、咽喉进路和到发时刻为一体的车站作业调度调整0-1整数规划模型。针对调度调整的强实时性,设计基于列生成的求解算法,并以长春西站为例进行验证。结果表明,该方法可在较短时间内得到满意的车站作业计划调整方案。     

A tabu search heuristic for the local container drayage problem under a new operation mode

This paper examines the Local Container Drayage Problem (LCDP) under an operation mode in which a tractor can be detached from its companion trailer and assigned to a new task. We have incorporated a set of temporal constraints into the classical VRP to realize this operation by utilizing the idle time available to tractors and coordinating the empty containers moving between customers. A tabu search algorithm is proposed. Some numerical experiments are conducted to assess the performance of the proposed algorithm, quantify the benefit of the new operation mode, and identify the conditions under which the mode is effective.