货运列车的编组调度问题

主要通过建立组合优化的模型,将原问题等价为一个TSP问题,运用遗传算法来求解.问题一:以到达场列车解体次序为决策变量,车辆"中时"最小为目标,分阶段建立组合优化模型;问题二:在问题一的基础上将含有军用车辆的列车和含有去向目的站点S1车辆的列车优先考虑解体,得到解编方案;问题三,将待解编列车的范围向后延伸2小时;问题四,将到达场列车中去向目的站点S1和S2以远的车辆分别排在目的站点E 3和E 4以南之间;问题五,由于编组完成的列车都能及时发出,当排完前一时段留下的车辆后,对于当前时段到达的列车采用随到随解策略进行解编;问题六,给出改进编组调度方案的建议和意见.     

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.     

邮路规划与邮车调度最优化理论研究

对小规模MTSP问题,建立了可精确求解方案的0-1规划模型,并在满足邮政运输需求的前提下给出了最佳方案.问题一首先以县支局、县局为顶点构建无向赋权图,通过Floyd算法求解各局间的最短距离;然后以Fijk为决策变量,以邮车工作时间、车辆运载能力为主要约束,建立以总空载损失费用最小为目标的0-1非线性规划模型,运用规划软件Lingo求解.问题二考虑到市邮路成本,我们采用分层规划策略,首先以市支局、县局为顶点构建无向赋权图,求解出最短路矩阵,建立以邮路运行成本最小为目标的0-1非线性规划模型IIA求解;然后,建立各县区的最短路矩阵,同样建立规划模型IIB求解各县运输方案.问题三由于县局地理位置不变,对区邮路无影响,故以全市各县支局为中心采用逐步最优方法对所有县区支局重新划分;然后采用模型IIB求解.第四问中考虑县局迁移,我们建立近似的启发式算法完成县局选址,并运用规划模型II求解的到新方案.最后,我们对两种区域划分调整方法还进行了定量的分析.     

应急物流LRP鲁棒双层优化模型研究

考虑到突发事件下受灾点对救灾物资需求的不确定性,针对应急物流设施的定位和车辆运输救灾物资路线进行协同研究,建立了应急物流设施定位-车辆路线选择问题(LRP)鲁棒双层优化模型.运用分散式决策方式下的转化定理,将所建立的含有不确定系数的层次关联协同优化模型进行确定性转化,并设计一种混合遗传算法对转化后的确定性双层规划模型进行求解,最后,通过实例验证了模型的合理性及算法的可行性.     

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.     

多维多目标模糊优选动态规划及其在资源分配中的应用

将目前所研究的一维模糊优选动态规划扩展为多维模糊优选动态规划。在求解多维多阶段问题时,采用遗传算法与模糊动态规划法相结合进行求解,保证了优化变量的全局最优性。在其中权系数的处理中,本文采用了主客观综合评定的方法,保证了数据的合理性及准确性。并且文中用此方法解决了多维多阶段多目标的资源分配问题。     

轿运车的装载和运输建模研究

主要围绕轿运车装载和运输的数学模型进行研究.在合理假设的基础上,将问题转化为多阶段的整数规划问题,并采用LINGO软件进行求解.当输入变量规模过于庞大时,采用启发式淘汰搜索多阶段优化模型,从而降低问题的求解难度.同时,提出一种局部整数分散连续的逐步优化方法,以提高优化效率.工作对降低运输成本和提高运输效率具有重要的意义.     

基于线性规则的应急资源配置随机优化模型与算法

考虑灾害发生时需求不确定的条件,建立了二阶段随机规划模型,解决针对突发事件的应急资源配置问题.不确定需求变量的分布函数信息难以获取,只能够根据历史数据获得随机变量的均值等信息.利用补偿变量为随机变量的线性决策规则将原应急资源配置模型进行转化,从而将原随机规划问题利用半无限线性规划问题进行逼近,且易于求解.数值试验表明,建立的应急资源配置模型是实际可行的,求解方法保证了解的稳定性.     

二维动态规划模型及其在陕西省七·五技改规划中的应用

在—般情况下,动态规划的阶段变量是一维的。但在实际工作中存在一类多阶段决策问题,由于这类问题的约束条件较多,造成状态变量的转移方向比较复杂,因而无法用一维动态规划模型求解。本文将动态规划数学模型中的阶段变量扩展到二维,通过建立二维动态规划模型对以上问题进行了研究。并且应用所建立的模型求解了一个实际的资源分配问题。     

混合动力公交车能量控制策略的优化模型

针对混合动力公交车在循环工况内功率需求的特点,建立了未来功率需求贝叶斯预测模型;利用2-阶段随机动态规划模型将大规模的随机动态规划问题简化为多个小规模的随机动态规划问题和一个确定型动态规划问题;对于随机动态规划模型的求解,给出了稀疏表示的降维方法,将复杂的泛函极值问题转化为常规的随机动态优化问题,并采用分布估计算法和计算资源最优配置算法的计算机仿真优化算法对随机动态优化问题进行求解;给出了基于查表的在线控制策略,为模型的实际应用进行了有益的探索。     

Freight transportation in railway networks with automated terminals: A mathematical model and MIP heuristic approaches

In this paper we propose a planning procedure for serving freight transportation requests in a railway network with fast transfer equipment at terminals. We consider a transportation system where different customers make their requests (orders) for moving boxes, i.e., either containers or swap bodies, between different origins and destinations, with specific requirements on delivery times. The decisions to be taken concern the route (and the corresponding sequence of trains) that each box follows in the network and the assignment of boxes to train wagons, taking into account that boxes can change more than one train and that train timetables are fixed.The planning procedure includes a pre-analysis step to determine all the possible sequences of trains for serving each order, followed by the solution of a 0-1 linear programming problem to find the optimal assignment of each box to a train sequence and to a specific wagon for each train in the sequence. This latter is a generalized assignment problem which is NP-hard. Hence, in order to find good solutions in acceptable computation times, two MIP heuristic approaches are proposed and tested through an experimental analysis considering realistic problem instances.     

Mixed integer programming models for dispatching vehicles at a container terminal

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0–1 integer programming models, Model I, Model II and Model III are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model I and Model II, as pure 0–1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model I. Model III is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model III is the best among these three models and the greedy algorithm is capable of solving large scale problems.     

A rolling horizon approach for disruption management of railway rolling stock

This paper deals with real-time disruption management of rolling stock in passenger railway transportation. We describe a generic framework for dealing with disruptions of railway rolling stock schedules. The framework is presented as an online combinatorial decision problem, where the uncertainty of a disruption is modeled by a sequence of information updates. To decompose the problem and to reduce the computation time, we propose a rolling horizon approach: rolling stock decisions are only considered if they are within a certain time horizon from the time of rescheduling. The schedules are then revised as time progresses and new information becomes available. We extend an existing model for rolling stock scheduling to the specific requirements of the real-time situation, and we apply it in the rolling horizon framework. We perform computational tests on instances constructed from real-life cases of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. We explore the consequences of different settings of the approach for the trade-off between solution quality and computation time.     

Dividends in finite time horizon

In this paper, we consider the classical risk model modified in two different ways by the inclusion of a dividend barrier. For Model I, we present numerical algorithms, which can be used to approximate or bound the expected discounted value of dividends up to a finite time horizon, t, or ruin if this occurs earlier. We extend this by requiring the shareholders to provide the initial capital and to pay the deficit at ruin each time it occurs so that the process then continues after ruin up to time t. For Model I, we assume the full premium income is paid as dividends whenever the surplus exceeds a set level. In our Model II, we assume dividends are paid at a rate less than the rate of premium income. Copyright © 2012 John Wiley & Sons, Ltd.     

Semi-Infinite Programming Approach to Continuously-Constrained Linear-Quadratic Optimal Control Problems

Consider the class of linear-quadratic (LQ) optimal control problems with continuous linear state constraints, that is, constraints imposed on every instant of the time horizon. This class of problems is known to be difficult to solve numerically. In this paper, a computational method based on a semi-infinite programming approach is given. The LQ optimal control problem is formulated as a positive-quadratic infinite programming problem. This can be done by considering the control as the decision variable, while taking the state as a function of the control. After parametrizing the decision variable, an approximate quadratic semi-infinite programming problem is obtained. It is shown that, as we refine the parametrization, the solution sequence of the approximate problems converges to the solution of the infinite programming problem (hence, to the solution of the original optimal control problem). Numerically, the semi-infinite programming problems obtained above can be solved efficiently using an algorithm based on a dual parametrization method.     

用Markov链模拟酒店逐月客房利用率的探讨

酒店逐月客房利用率的预测,对于安排来年人员及物质、设备等是至关重要的,也是计算酒店经济利润,进行成本核算不可缺少的参数.由于客人入住是一个随机过程,难以用准确的分布函数来描述,但作出某些合理的假设后,可将其变为平稳随机过程.用M arkov自回归模型可生成模拟未来年的逐月客房利用率系列,以满足酒店规划、计划、市场营销等方面的需要.以乌鲁木齐市伊斯兰酒店为例,用M arkov模型进行了模拟.     

Resource allocation with stochastic optimal control approach

A control-theoretic decision making system is proposed for an agent (decision maker) to “optimally” allocate and deploy his/her resources over time among a dynamically changing list of opportunities (e.g., financial assets), in an uncertain market environment. The solution is a sequence of actions with the objective of optimizing total reward function. This control-theoretic approach is unique in a sense that it solves the problem at distinct time epochs over a finite time horizon and strategies are discovered directly. Rather than basing a decision making system on forecasts or training via a reinforcement learning algorithm using current state data, we train our system via a Q-learning algorithm using Geometric Brownian Motion as an asset price function. While the above problem is quite general, we focus solely on the problem of dynamic financial portfolio management with the objective of maximizing the expected utility for a given risk level. The performance functions that we consider for our system are realized mean return, drawdown and standard deviation. We find that our model achieves a better return and drawdown compared to a known market index as a benchmark.     

KdV-Burgers-Kuramoto系统的渐近吸引子

研究了 KdV-Burgers-Kuramoto 方程的渐近吸引子,即利用正交分解法构造一个有限维解序列。首先用数学归纳法证明了该解序列不会远离方程的整体吸引子,接着证明解序列在长时间后无限趋于方程的整体吸引子,最后给出渐近吸引子的维数估计。