货运列车编组调度问题的模型与算法研究

从双向编组站运输生产实际情况出发,以最大化车站发出车数和最小化车辆在站平均停留时间(中时)为目标,综合考虑解体、编组调机能力限制、到发列车车流接续、车流在站停留时间约束的影响,建立了车站货运列车编组调度问题的多目标非线性混合整数规划模型,结合该优化模型难以求解的特点,将编组调度问题分解为配流、待解车列解体和待编车列编组三个子问题,进而设计了求解该问题的分层启发式算法,对正常和特殊运输组织条件下的列车编组调度问题进行了求解.     

编组站调度计划0-1规划法

针对货车编组问题,采用半分离式两阶段0-1线性规划模型对各阶段联合求解,对局部最优解采用调度时序图可视化表述.首先,对无、有调车辆分离,无调车采用启发式安排.有调车推峰顺序可以转化为零件加工问题,以驼峰总工作量最大、等待时间最小为目标建立模型I.列车解体时间与解体方向数成正比增长,但在未确定具体解体方案时无法确定(即模型I的独立),通过在模型II中对解体时间模糊化来处理两步独立的缺陷,从而达到两步规划的连续特性.车辆新编,决策变量属于多维结构,通过将多维稀疏变量转化为一维序列,有效解除其稀疏特性,形成二维决策变量建立规划模型II直接求解.其次,通过仿真创建模拟数据,运用主模型求解,得到了驼峰是编组站主要瓶颈的结论.最后,我们还对铁路资源的紧缺性、编组效率建模给出了较详细改进措施.     

货运列车的编组调度问题的研究

对于货运列车的编组调度问题,建立了以压缩中时和增加运量为双目标、多约束的0-1规划模型,采用逐步紧缩中时约束的方法得到了问题一的调度方案.在此基础上对装载特别物资的车辆施加更强的中时约束得到了问题二的调度方案.提出了列车匹配系数的概念并建立了列车最优配对原则,根据该原则对列车进行优化编组得到了问题三的调度方案.基于问题二的数据转换处理得到了问题四的调度方案.     

货物列车的编组调度问题

研究的是货物列车的编组和调度问题.通过对问题的深入研究,设计了一种车辆编组调度方案的算法.按照这种算法,在数据处理的基础上利用VC编写每个问题的处理程序,实现了对列车的快速安全高效的调度.对每个问题进行处理,都得到符合要求的结果.问题一首先对整个车辆编组调度的问题进行分析,在尽量保证新组装列车满载的基础上,使每班的中时尽可能少.为此,本文解决了两个关键问题:一是选车问题,二是拆解重组的问题.采用梯形方案对列车车辆进行编队重组,对选车问题主要采用按照时间先后顺序的选车方案,然后通过启发式算法配合遗传算法的选车方案对按时间先后顺序的方案进行检验.从编写的VC程序的运行结果来看,两种方案都可得到满意的结果,遗传算法得到的结果更为合理.另外,为了达到中时最短,采用双推双滑的方式利用驼峰线,提高了调度效率,并在驼峰线和编组道之间加入了碰撞检验模块,保证了列车调度时的安全性.问题二的求解是在问题一的基础上对待拆列车按优先级进行分类.对优先级高的列车先进行拆解.救灾车辆最高,其次是军列和发往S1的车辆,最后是一般车辆.问题三的处理主要是在问题二的基础上,通过提前获得列车的相关信息来决定编组场的列车离开编组场的时刻,从而缩短车辆的中时.问题四在原有模型基础上对编组方案进行了修改,利用编写的VC程序重新计算了每班的中时和列车的调度方案.问题五主要分析了整个系统瓶颈所在,分析了提高资源利用率的可行性.最后,通过对站名的调整,达到了对地质灾害等对铁路系统的破坏突发情况的有效处理,并且进一步分析了如何提高车站的效率的调度方案和建议.     

制订列车编组计划的整数规划法

<正> 一、问题的提出列车编组计划是组织铁路车流运输的具体方案,也是全路各站的统一作业计划.它规定各编组站要编组的列车种类、到达站和各编组站的改编作业任务.这个问题是车流组织的核心,对加速车辆周转,提高运输效率有着直接的影响.因而建立科学的制订列车编组计划的计算方法,长期一直为铁路运营工作者们所重视,成为改善铁路运输组织工作的重要理论和实际课题之一.     

校车站点及线路的优化设计

以高校新校区教师校车站点及线路安排为对象,首先针对乘车站点建立了双目标非线性规划模型,其中目标函数包括乘客到达站点的距离偏差最小与所有乘客到达站点的总的距离最小两个方面;站点确定后针对车辆数最少、车辆行驶的总距离最短、各辆车的运行距离均衡及各辆车的负荷均衡这4个目标建立针对线路优化的多目标非线性规划模型,并给出了解决这类问题的启发式优化算法.与目前国内外研究相比较,该模型与算法更实际,更具体的给出了问题的解答.     

多车型车辆调度问题的优化模型

基于第十一届"华为杯"全国研究生数学建模竞赛E题第五问,针对一类多车型多目的地的整车物流运输调度问题,先直接计算完成总任务所需的车辆数来阐明该题的最优解的下界限为113辆,再对原始数据进行预处理,基于对乘用车的分类与排样算法,筛选出每种轿运车的M种装载方案代表,再对目的地位置及结合各目的地的任务需求,确定出3条不绕行路线,根据启发式调整优化算法,并以轿运车使用量最少及总行驶里程最短为优化目标,建立了多目标整数规划模型进行求解,最优可行解为114辆,其中1-1型91辆,1-2型18辆,2-2型5辆.     

带集货和配送的多站点VRP优化算法研究

带集货和配送的多站点车辆路线问题(M DVRPPD)是经典VRP的扩展,是多个站点和若干客户既有需求又有供给的VRP问题.研究了该问题的模型并提出了求解该问题的多阶段启发式算法,即先用临界客户的思想把多站点转换为单一站点问题,再使用基于SFC的分组方法来构造初始解,并运用3-opt算法优化回路,之后采用插入算法改善解的可行性,从而得到最终优化解.最后通过实例计算证明了该方法解决M DVRPPD问题的实用可行性和科学有效性.     

时变单车路径优化模型及动态规划算法

车辆路径问题由于其广泛的应用领域及经济价值而成为学术研究热点。然而,在已有的研究文献中,车辆的速度时变与服务多任务特性很少被关注。本文讨论了具有这两个特性的单车路径优化问题。建立了以送货完成时间最早为优化目标的时变单车送货路径优化模型。由于很难获得该模型的精确解,本文提出了一种贪婪补货策略压缩原问题解空间,设计动态规划算法给出了车辆行驶时间满足FIFO规则的送货顺序近似最优解。数值算例验证了该算法所得到的解仅是原问题的近似最优解这一结论。算例同时表明优化配送时间随着车辆装载能力的增大而缩短,并在车辆装载能力超过所有客户配送总需求时实现最短配送时间,即,使用较大装载能力车辆能节约更多配送时间。     

货运列车的编组调度问题研究

针对货运列车的编组调度问题,根据实际情况和理论研究的需要,首先对模型做了分析和假定.在此基础上,以中时最短为优化目标,给出了基于0-1整数规划和多目标优化理论的优化数学模型,实现了理论上可行的编组调度方案.     

Approximating the chance-constrained capacitated vehicle routing problem with robust optimization

4OR - The Capacitated Vehicle Routing Problem (CVRP) is a classical combinatorial optimization problem that aims to serve a set of customers, using a set of identical vehicles, satisfying the...     

Metaheuristics for vehicle routing problems with three-dimensional loading constraints

This paper addresses an important combination of three-dimensional loading and vehicle routing, known as the Three-Dimensional Loading Capacitated Vehicle Routing Problem. The problem calls for the combined optimization of the loading of freight into vehicles and the routing of vehicles along a road network, with the aim of serving customers with minimum traveling cost. Despite its clear practical relevance in freight distribution, the literature on this problem is very limited. This is because of its high combinatorial complexity.     

求解0-1背包问题的细菌觅食算法

0-1背包问题是组合优化中的一个典型NP难题,介于其具有广泛的实际应用,有效的解决该问题具有非常重要的意义.给出了一种新的群智能算法—细菌觅食算法,对0-1背包问题进行求解.经模拟仿真验证了该算法的有效性,并将其结果与其他方法进行对比分析.     

Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time Windows (VRPTW) is a combinatorial optimization problem. It deals with route planning and the distribution of goods from a depot to geographically dispersed customers by a fleet of vehicles with constrained capacities. The customers’ demands are known and each customer has a time window in which it has to be supplied. The time windows are assumed to be soft, that means, violations of the time windows are allowed, but associated with penalties. The problem is to organize the vehicle routes optimally, i.e. to minimize the total costs, consisting of the number of used vehicles and the total distance, and the penalties simultaneously. Thus, the problem is formulated as a bicriterion minimization problem and heuristic methods are used to calculate approximations of the Pareto optimal solutions. Experimental results show that in certain cases the allowance of penalties leads to significant savings of the total costs.     

On the capacitated vehicle routing problem

 We consider the Vehicle Routing Problem, in which a fixed fleet of delivery vehicles of uniform capacity must service known customer demands for a single commodity from a common depot at minimum transit cost. This difficult combinatorial problem contains both the Bin Packing Problem and the Traveling Salesman Problem (TSP) as special cases and conceptually lies at the intersection of these two well-studied problems. The capacity constraints of the integer programming formulation of this routing model provide the link between the underlying routing and packing structures. We describe a decomposition-based separation methodology for the capacity constraints that takes advantage of our ability to solve small instances of the TSP efficiently. Specifically, when standard procedures fail to separate a candidate point, we attempt to decompose it into a convex combination of TSP tours; if successful, the tours present in this decomposition are examined for violated capacity constraints; if not, the Farkas Theorem provides a hyperplane separating the point from the TSP polytope. We present some extensions of this basic concept and a general framework within which it can be applied to other combinatorial models. Computational results are given for an implementation within the parallel branch, cut, and price framework SYMPHONY. Received: October 30, 2000 / Accepted: December 19, 2001 Published online: September 5, 2002 Key words. vehicle routing problem – integer programming – decomposition algorithm – separation algorithm – branch and cut Mathematics Subject Classification (2000): 20E28, 20G40, 20C20     

A computational study of exact knapsack separation for the generalized assignment problem

The Generalized Assignment Problem is a well-known NP-hard combinatorial optimization problem which consists of minimizing the assignment costs of a set of jobs to a set of machines satisfying capacity constraints. Most of the existing algorithms are of a Branch-and-Price type, with lower bounds computed through Dantzig–Wolfe reformulation and column generation.     

航空器供油问题中完全逆序类中的多项式时间可解类

航空器供油问题是一类非线性组合优化问题,其目标函数为分式形式,该问题目前不存在多项式时间算法,也未被证明是NP完全问题。一般可以用置换来刻画n架飞机的一个供油顺序。该问题中有一类实例被称为“完全逆序类”,“完全逆序类”用动态规划算法求解计算时间为O(n2ⁿ),具有指数时间复杂度。本文通过对该“完全逆序类”问题做进一步分析,发现在“完全逆序类”中也存在着多项式时间可解的情况。定理1研究一类一次可解的情况,若问题满足定理1的条件,则求解一次即可找到其最优解;定理2研究一类多项式时间可解的情况,当问题满足定理2的条件时,其最优解可在多项式时间内获得。     

Ants can orienteer a thief in their robbery

The Thief Orienteering Problem (ThOP) is a multi-component problem that combines features of two classic combinatorial optimization problems: Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the given time constraint and the interaction between its components. We propose an Ant Colony Optimization algorithm together with a new packing heuristic to deal individually and interactively with problem components. Our approach outperforms existing work on more than 90% of the benchmarking instances, with an average improvement of over 300%.