乘客到达时间不确定的机场动态拼车策略与算法研究

网约车拼车服务作为共享经济领域重要应用,已成为国内外研究热点。针对机场在线拼车平台运营中乘客等待时间过长和车辆行驶成本较高的突出问题,本文提出前瞻式动态拼车匹配策略。该策略将未来随机到达乘客信息纳入当前已到达乘客的拼车匹配决策中,建立了乘客匹配与车辆路径联合优化两阶段随机规划模型。为了在动态环境中实时产生高质量的匹配与路径规划方案,首先基于贝叶斯估计压缩乘客随机到达情景空间,建立了问题的确定性近似最优模型。为了快速求解模型,提出基于订单目的地和乘客期望到达时间相似度的匹配规则,并以此开发改进的差分进化算法。最后,基于某拼车平台真实订单数据,通过对比测试验证了前瞻式匹配策略和改进差分进化算法的有效性与计算效率。     

校车安排问题

探讨如何安排校车运行使得教师和工作人员尽量满意的问题.首先建立动态规划模型和选址规划模型,求出合理站点位置及其总距离.然后用归一法定义满意度与距离的函数关系,考虑各区域人数,建立选址规划模型.得到合理站点位置和总满意度.之后建立双目标非线性规划模型,利用量纲分析法给出权重,以此求出合理乘车位置和满意度.最后对问题进行推...     

连接主干道的多站点线路规划

将平面上多个已知站点和一条主干道相连接,使线路总长度最小,这是具有广泛应用的线路规划问题.文章从研究两点问题和三点结构的基础模型入手,设计出多站点线路规划树的优化调整方法.进一步构造出以整体调整为特征的定位反演算法,具有计算量小,操作方便的优点.     

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.     

考虑均衡性的不确定时间车辆调度问题研究

本文针对车辆调度实际运行过程中时间的不确定性问题,提出了包含时间窗口、车辆容量约束的配送服务线路随机规划模型,以最小化调用的车辆数目和运行距离,降低顾客的不满意度并且尽可能保证每条路线的均衡性。结合模型,给出了基于禁忌搜索的混合启发式算法,并且生成多个算例,依据算例结果说明模型和算法优越性,同时说明可以在不降低顾客满意度和不提高总运输成本的基础上,降低各条线路之间的时间差异。     

多类型公共自行车调运问题

公共自行车是我国正大力发展的低碳交通出行模式,加强公共自行车调运优化是提升自行车出行吸引力的关键要素。通过对公共自行车调运背景分析,提出了一类多类型公共自行车的调运优化问题。针对现实生活中租赁站点内公共自行车不均衡的情况,建立了以总成本最小为目标的混合整数线性规划模型,并提出一种改进的混合禁忌搜索对问题进行求解。通过数值实验分析了问题特性并验证了算法性能。实验结果表明非均衡惩罚系数决定了租赁站点各类自行车的装卸载数量,并影响了调配车辆的运行路线,是实现多类型公共自行车均衡优化的关键因素。不同类型自行车的替代策略使得调运决策更加灵活。混合禁忌搜索可以求解更大规模的问题,并能在短时间内求得较好质量的解。     

带模糊时间窗的配送问题多目标优化研究

针对配送多目标优化问题,综合考虑车辆使用数、运输总里程和客户服务水平,基于双层规划的思想,解决了车辆数函数和运输里程函数的区间伸缩指标问题,并引入客户不满意度的模糊隶属度函数来描述配送服务水平。通过去量纲将三个优化目标转化为总目标函数的功效函数,并运用模糊层次分析法对三个函数分配权重,建立以车辆使用数最少、运输总里程最小、客户不满意度最低的标量化多目标模型,并运用模拟退火算法验证了模型的合理性和普适性。     

具有模糊目标的多目标双矩阵对策集结与求解

针对不确定性多冲突环境,建立了多个具有模糊目标的多目标双矩阵对策的综合集结模型.在假定局中人各模糊目标的隶属函数为线性函数的情形下,基于总体模糊目标的可达度,给出了纳什均衡解的定义,并应用粒子群优化算法对集结模型求解.最后,给出一个军事例子说明了模型的实用有效性和粒子群优化算法求解的高效性.     

基于两阶段启发式算法的物流配送选址-路径问题研究

为了解决配送中心选址与带时间窗的多中心车辆路径优化组合决策问题,利用双层规划法建立了配送中心选址与车辆路径安排的多目标整数规划模型,针对该模型的特点,采用两阶段启发式算法进行了求解。首先,通过基于聚集度的启发式算法对客户进行分类,确定了备选配送中心的服务范围;然后,基于双层规划法,以配送中心选址成本最小作为上层规划目标,以车辆配送成本最小作为下层规划目标,建立了多目标整数规划模型;最后,利用改进的蚁群算法进行了求解。通过分析实例数据和Barreto Benchmark算例的实验结果,验证了该模型的有效性和可行性。     

基于双层规划的随机补能车辆均衡分配研究

随着我国人均汽车保有量不断增加,导致居民对能源需求持续提升,进而加剧了补能车辆在有限站点的集聚程度.基于实时交通信息,以路网补能总阻抗最小化为目标,建立了随机车辆补能分配的双层模型,其中上层为路网车辆均衡模型,下层为个体补能决策模型.结合驾驶员的个人偏好,考虑油价和站点排队程度两个诱导因素,设计了GA-MSA算法进行求解.最后在Nguyen-Dupuis路网上对模型和算法的有效性进行验证,结果证明本文所提出的模型及算法具有较好的适用性和补能优化效果.     

弹性需求下城市公交网络服务的优化

对具有弹性需求的城市公交网络系统进行了票价结构与发车频率组合的优化。考虑到公交定价和发车频率会影响乘客需求以及乘客对路径的选择行为,将这一问题描述为一个双层规划问题,上层是寻求社会福利最大的优化问题;下层考虑了乘客的出行选择行为,为弹性需求下乘客在城市公交网络上流量分布的随机用户平衡分配模型。鉴于双层规划问题的非凸性,运用模拟退火算法对模型进行求解,并给出一个仿真算例说明提出的模型和算法的合理性。     

多方式弹性需求下公交定价双层规划模型

为描述多方式城市交通网络下公交定价与出行选择行为的相互作用与影响,将出行方式选择与路径选择涵盖于同一网络,建立了上层模型分别以企业利润最大化、乘客出行成本最小化和社会福利最大化为目标函数,下层模型为多方式弹性需求随机用户配流模型的公交定价双层规划模型。运用改进遗传算法对模型整体进行求解,下层模型采用综合对角化算法和MSA算法的组合求解算法。最后,设计了一个算例以说明模型应用。结果表明:运用双层规划模型所确定的公交票价较传统静态票价可使政府、企业及出行者三方都获得更高收益,且上层模型以社会福利最大化为目标函数能代表社会群体中多数人利益,优化效果最为理想。     

公交车调度的数学模型

通过建模求解了2001年大学生数学建模竞赛B题,文中根据在最大程度上照顾乘客和公司双方的利益及调度方便的要求把该问题归结为多目标决策问题,利用逐段最优算法求出最优调度方案,并求得了发车时刻表及所需最少车辆数.     

The Design of Routes,Service Frequencies,and Schedules for a Municipal Bus Undertaking: A Case Study

This paper describes a case study of the reorganization of a municipal bus undertaking. The terms of reference were to consider reorganizing the route structure, to advise on frequencies to operate on the chosen routes, to compile timetables, and to design bus schedules so that the recommended system could be implemented with the minimum number of active buses. Theoretically, all of these variables can interact and one global model should be used to optimize the entire system simultaneously. In practice, the components of this problem had to be uncoupled and tackled separately. An heuristic model designed to provide a "good" route network was developed. Service frequencies were allocated to routes so that a measure of service to passengers was maximized. Bus timetables were compiled using conventional methods and a linear programming model used to assign individual buses to journeys. A new scheme based on the recommendations of the study has been implemented. It is expected that it will reduce the annual operating deficit by [pound]35,000 without appreciably decreasing the overall level of service offered.     

资源受限下森林火灾应急救援多目标调度优化

许多森林火灾由于救援资源受限而不能在第一时间扑灭,导致火灾扩大蔓延,进而造成更大的森林资源损失。因此,在救援资源受限情形下,如何对消防救援车辆进行合理的调度安排以快速和低成本地扑灭火灾已成为亟待解决的现实问题。本文研究了一类资源受限下森林火灾应急救援多目标调度优化问题,为该问题构建了多目标混合整数非线性规划模型,优化目标为同时最小化总灭火救援时间和救援车辆总行驶距离。为有效求解该问题,首先将上述非线性模型等价转化为线性模型。然后提出ε-约束法和模糊逻辑相结合的算法对问题进行求解。最后,以大兴安岭山发生的火灾案例和随机生成仿真算例对模型和算法有效性进行验证,结果表明所提出的模型和算法能够有效解决资源受限下森林火灾应急救援问题,并为决策者提供最优的消防调度方案。     

Integrating link-based discrete credit charging scheme into discrete network design problem

In this paper, we present an optimization model for integrating link-based discrete credit charging scheme into the discrete network design problem, to improve the transport performance from the perspectives of both transport network planning and travel demand management. The proposed model is a mixed-integer nonlinear bilevel programming problem, which includes an upper level problem for the transport authority and a lower level problem for the network users. The lower level sub-model is the traffic network user equilibrium (UE) formulation for a given network design strategy determined by the upper level problem. The network user at the lower level tries to minimize his/her own generalized travel cost (including both the travel time and the value of the credit charged for using the link) by choosing his/her route. While the transport authority at the upper level tries to find the optimal number of lanes and credit charging level with their locations to minimize the total system travel time (or maximize the transportation system performance). A genetic algorithm is used to solve the proposed mixed-integer nonlinear bilevel programming problem. Numerical experiments show the efficiency of the proposed model for traffic congestion mitigation, reveal that interaction effects across the tradable credit scheme and the discrete network design problem which amplify their individual effects. Moreover, the integrated model can achieve better performance than the sequential decision problems.     

Optimal coordination of directional over-current relays using informative differential evolution algorithm

Growing interconnection in distribution system creates new problem for protection engineers. Particularly the design of overcurrent relay coordination in such system is an independent area of research. With the availability of new artificial based optimization algorithm relay coordination research gain a new momentum. Well established artificial based optimization algorithm such as genetic and particle swam optimization are successfully applied for such applications. This paper discusses the application of informative differential evolution algorithm with self adaptive re-clustering technique for selection of TDS and PSM for optimal coordination of directional overcurrent relays. Both continuous as well as discrete version of informative differential evolution algorithm are used for optimization of relay setting. Proper combination of backup relays for each primary relay are identified by using LINKNET graph theory approach. Coordination of directional overcurrent is developed for 9 bus and IEEE 30 bus distribution systems. The aim of problem is to minimize the total operating time of primary relays and eliminate the miscoordination among the primary and backup relay pairs. Discrete types of settings for electromechanical types of relay are also discussed in this paper. Moreover, the relay coordination problem is modified for providing optimal coordination time interval between 0.2 and 0.8 s among all primary and backup relays pairs. The results are compared with hybrid of genetic algorithm – nonlinear programming and sequential quadratic programming. Digsilient power factory software is used for verification of result.     

A metaheuristic for the school bus routing problem with bus stop selection

Existing literature on routing of school buses has focused mainly on building intricate models that attempt to capture as many real-life constraints and objectives as possible. In contrast, the focus of this paper is on understanding the joint problem of bus route generation and bus stop selection – two important sub-problems – in its most basic form. To this end, this paper defines the school bus routing problem (SBRP) as a variant of the vehicle routing problem in which three simultaneous decisions have to be made: (1) determine the set of stops to visit, (2) determine for each student which stop (s)he should walk to, and (3) determine routes that lie along the chosen stops, so that the total traveled distance is minimized. An MIP model of this basic problem is developed.     

Solving a school bus scheduling problem with integer programming

In many rural areas in Germany pupils on the way to school are a large if not the largest group of customers in public transport. If all schools start more or less at the same time then the bus companies need a high number of vehicles to serve the customer peak in the morning rush hours. In this article, we present an integer programming model for the integrated coordination of the school starting times and the public bus services. We discuss preprocessing techniques, model reformulations, and cutting planes that can be incorporated into a branch-and-cut algorithm. Computational results show that in our test counties a much lower number of buses would be sufficient if the schools start at different times.