时相关车辆路径规划问题的改进A*算法

时相关车辆路径问题是研究时变路网环境下的车辆路径优化问题.首先,分别采用阶跃函数和分段连续函数描述不同路径上的跨时段行驶速度和威胁度,将路径时间指标和路径威胁指标表示成时相关函数;其次,为提高搜索效率,对传统A*算法进行改进,在启发函数中增加了最短路径中当前结点的父结点信息,构造了包含里程指标、时间指标和威胁指标的时相关启发函数;最后,构造了包含100个结点、190条路径的车辆机动保障路网模型,通过仿真验证了该算法的有效性.     

带时间窗动态车辆路径问题的优化调度策略

针对带时间窗动态车辆路径问题(DVRPTW),根据优化时间点的选取建立DVRPTW的一系列静态子模型,研究优化调度策略,分别是基于新顾客插入后的重复优化方法、批处理的方法或两者的混合。通过定义紧急顾客,提出一种新的紧急顾客插入和分批驱动调度策略,该策略只需实时插入紧急顾客,具有快速响应、计算负荷小和路径计划更新频率低的优点。仿真对比实验比较了4种策略的优劣,并探讨分批优化间隔长度对调度性能的影响。     

A Novel Model for the Time Dependent Competitive Vehicle Routing Problem: Modified Random Topology Particle Swarm Optimization

This paper presents a novel model for a time dependent vehicle routing problem when there is a competition between distribution companies for obtaining more sales. In a real-world situation many factors cause the time dependency of travel times, for example traffic condition on peak hours plays an essential role in outcomes of the planned schedule in urban areas. This problem is named as “Time dependent competitive vehicle routing problem” (TDVRPC) which a model is presented to satisfy the “non-passing” property. The main objectives are to minimize the travel cost and maximize the sale in order to serve customers before other rival distributors. To solve the problem, a Modified Random Topology Particle Swarm Optimization algorithm (RT-PSO) is proposed and the results are compared with branch and bound algorithm in small size problems. In large scales, comparison is done with original PSO. The results show the capability of the proposed RT-PSO method for handling this problem.     

An Ant Colony algorithm hybridized with insertion heuristics for the Time Dependent Vehicle Routing Problem with Time Windows

This paper presents an Ant Colony System algorithm hybridized with insertion heuristics for the Time-Dependent Vehicle Routing Problem with Time Windows (TDVRPTW). In the TDVRPTW a fleet of vehicles must deliver goods to a set of customers, time window constraints of the customers must be respected and the fact that the travel time between two points depends on the time of departure has to be taken into account. The latter assumption is particularly important in an urban context where the traffic plays a significant role.     

A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem

In this paper, a memetic algorithm with competition (MAC) is proposed to solve the capacitated green vehicle routing problem (CGVRP). Firstly, the permutation array called traveling salesman problem (TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor (kNN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search (VNS) framework and the simulated annealing (SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station (AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-of-experiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.      

面向带时间窗车辆路径问题的PGSA算法优化

VRPTW问题是带时间窗约束的车辆路径问题,该问题的求解通常被应用到物流的路径规划环节,现实意义突出,属于NP难题,计算量随问题规模增大呈指数增长。PGSA算法是模拟植物生长信息和分支模式的启发式算法,被用于求解组合优化问题。本文以配送总路程最短为目标构建VRPTW问题的约束模型,在原始PGSA算法的基础上,使用双阶段的搜索方案,提高初始解的质量,设计有向生长机制和局部解跳出机制更改原算法生长点的生长策略,提高了PGSA算法的搜索效率。通过在标准数据集上的实验分析,改进后的PGSA算法相比原始PGSA算法,能达到更好的收敛结果,求解效率更高,是一种有效的求解方法。     

求解带时间窗车辆路径问题的动态混合蚁群优化算法

为求解带时间窗车辆路径问题,针对传统蚂蚁遗传混合算法中参数静态设置、冗余迭代及收敛速度慢等缺点,提出一种动态混合蚁群优化算法( DHACO)。该算法首先借助最大最小蚁群得到初始解,利用蚁群优化算法求解带时间窗车辆路径问题的基本可行解。然后采用遗传算法交叉和变异操作对局部解和全局最优解进行二次优化,从而得到最优解。最后利用蚂蚁遗传混合算法融合策略,动态交叉调用蚂蚁算法、遗传算法,根据云关联规则自适应控制蚁群算法参数。 DHACO有效减少无效迭代次数,加快收敛速度。仿真结果表明,与其他相关的启发式算法相比,DHACO优于某些实例的已知最优解。     

基于带时间窗口车辆路径问题的蚁群算法

带时间窗口的车辆路径问题（VRPTW）是一个NP-Complete优化问题。VRPTW的主要目标在于利用最少的车辆数以及最短的行程来服务客户,客户有固定的需求和被服务的时间限制。基于该问题提出了一种并行多蚁群算（PMACS-VRFTW）：首先利用QUICK-ACS生成初始解,然后利用ACS-VEI和ACS-TIME分别优化车辆数和行程距离。试验表明,所提出的算法基于Solomon的VRPTW基准实例获得了很好的结果。     

轴辐式网络下多重时效要求的车辆调度问题

公路运输在多式联运中发挥着不可替代的重要作用,车辆调度问题日益成为制约公路运输质量和效率的主要因素之一。针对零担快运和快递干线运输的特点,考虑车辆容量限制和节点任务的多重时效性约束,建立了轴辐式网络下的车辆调度模型,设计了基于启发式调度规则的节约算法进行求解。通过中国邮政广州邮区的运营数据进行算例分析,计算结果表明,显著提高了车辆有效使用效率和运营成本,验证了模型和算法的有效性。最后分析了网络辐射范围对运输效率和经济性的影响及车辆有效使用效率与期望工时之间的关系,为公路干线运输车辆调度提供决策支持。     

限制期条件下应急车辆调度问题的模糊优化方法

由于应急调度问题中存在时间紧迫性与应急出救点数目相互矛盾的目标,因此给出一个反映决策者偏好的折衷方案十分必要。从实际应用出发,运用模糊优化方法研究限制期下的多出救点组合模型求解问题。     

多目标动态车辆路径问题建模及优化

针对物流配送中动态车辆路径优化问题,综合考虑动态需求、路网影响、车辆共享、时间窗以及客户满意度,建立了多目标动态数学规划模型,该模型能更好地描述现代物流配送问题.同时,提出一种两阶段求解策略,第一阶段采用多目标混合粒子群优化算法获取预优化阶段Pareto最优解,采用改进的粒子状态更新策略并融合模拟退火操作提升粒子群搜索性能,采用自适应网格技术保持解的分布性;第二阶段对客户的需求变化采用贪婪插入和变邻域搜索进行实时路径调整.实验表明,该算法在解空间中有更好的探寻能力,并能快速收敛到全局最优,满足动态路径优化实时性要求.     

前摄性车辆路径问题及其遗传算法求解

通过对现有文献中需求信息不确定的动态车辆路径问题，在不确定需求预测和求解算法的基础上，建立了多维数据层客户需求预测方法和前摄性实时控制方法，讨论了潜在客户的响应准则。以总运输成本最小为目标，构建了引入前摄性实时控制方法求解动态车辆路径问题的数学模型，改进了遗传算法对该模型进行求解。应用京东在重庆地区的客户点的配送数据及两阶段综合前摄性调整策略，验证了设计算法的性能，实验结果表明设计的模型及算法可以对客户需求进行更及时有效的响应。     

Arc-Guided Evolutionary Algorithm for the Vehicle Routing Problem With Time Windows

This paper presents an arc-guided evolutionary algorithm for solving the vehicle routing problem with time windows, which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of capacitated vehicles within fixed time intervals. The objective is to minimize the fleet size following routes of minimum distance. The proposed method evolves a population of ${mu}$ individuals on the basis of an $({{mu}+{lambda}})$ evolution strategy; at each generation, a new intermediate population of ${lambda}$ individuals is generated, using a discrete arc-based representation combined with a binary vector of strategy parameters. Each offspring is produced via mutation out of arcs extracted from parent individuals. The selection of arcs is dictated by the strategy parameters and is based on their frequency of appearance and the diversity of the population. A multiparent recombination operator enables the self-adaptation of the strategy parameters, while each offspring is further improved via novel memory-based trajectory local search algorithms. For the selection of survivors, a deterministic scheme is followed. Experimental results on well-known large-scale benchmark datasets of the literature demonstrate the competitiveness of the proposed method.      

分批配送的有时间窗车辆路径问题的遗传算法

给出了分批配送的有时问窗车辆路径问题（BVRPTM）的数学模型。通过引入改进的路径可行化方法和MRC交叉算于，构造了一种适于求解BVRPTM的遗传算法。实验结果表明，该算法能有效地解决BVRPTM，并取得了较好的优化结果。     

Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.     

带工作时间与时间窗的开放式车辆路径问题

在BtoC电子商务物流配送活动中,配送公司外包车辆和配送人员上班具有时间限制。为求解带工作时间与时间窗约束的开放式车辆路径问题(OVRPTWWT),构建其混合整数规划模型,给出一种求解该问题的非代际克隆选择算法,对算法中抗体的更新与抑制策略进行改进,设计出新的克隆选择方法。通过实验验证了该模型与OVRPTWWT非代际克隆选择算法的有效性。     

基于遗传算法求解带时间窗的车辆路由问题

提出一种改进的遗传算法，用于求解带时间窗的车辆路由问题．在算法中采用了直观的自然数缟码机制、三复本锦标赛的选择方法和改进的启发式交叉算子，实验表明该方法用于求解带时间窗的车辆路由问题的有效性．     

基于多目标遗传算法求解时间窗车辆路径问题

有别于传统的单目标方法,将带时间窗约束的车辆路径问题描述成为一个多目标最优化问题,并为之提出了一种多目标遗传算法。在算法中设计了擂台法则作为构造非支配集的方法,提出了可变爬山率的局部爬山法,并通过将组合种群分成多层非支配集来实现精英保留策略。实验结果表明,该算法能有效地求解车辆路径问题并且为决策者提供了强有力的决策支持。     

基于启发式带时间窗的车辆路径规划问题求解

车辆路径规划问题广泛地存在于现代物流行业中,该问题属于NP难的组合优化问题.随着客户需求的多样化、道路限行等因素的影响,该问题变得更加的复杂,采用传统的组合优化方法和运筹学方法往往难以求解.本文对一类常见的带时间窗的车辆路径规划问题进行了研究,根据时间窗参数来调整客户的优先级,以减少车辆的等待时间,由此改进了几个常见的启发式算法,并对56个常见的车辆路径规划问题进行了测试,实验结果表明,改进的节约算法在带容量约束的车辆路径问题中效果较好,改进的插入法则在带时间窗的车辆路径问题中具有优越性,另外,改进的启发式算法在4个测试用例上使用更多车辆时可使总路程优于已知最优值.     

基于GPS/GIS协同的动态车辆调度和路径规划问题研究

从物流行业信息化和智能化发展的需求出发,利用以物联网为代表的现代信息和通信技术,设计了GPS/GIS协同下的智能车辆监控和调度系统。同时,基于该调度系统具有的信息实时获取和智能处理能力,考虑配送车辆及客户需求等相关实时信息对车辆调度和路径规划的影响,构建了基于实时信息且带时间窗的动态车辆路径问题(DVRPTW)混合整数规划(MIP)模型。结合模拟实验,通过混合遗传算法寻优对车辆配送路径进行动态调整和优化,为物流行业降低企业运营成本、提高物流配送效率、改善物流服务质量提供借鉴和参考。