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。     

基于改进禁忌搜索算法的车辆路径优化

为解决车辆路径优化问题,提出改进禁忌搜索算法.首先,采用车辆一需求分配结构,将整个车辆路径问题分解成若干子问题,然后用禁忌搜索算法求解每个子问题,最后从所有子问题的最优解中选取全局最优解,并通过具有代表性的算例试验和分析.仿真试验结果表明,该算法拓展了搜索空间,提高了最优解的质量,能够有效地解决车辆路径优化问题.     

基于遗传模拟退火算法的战时物流配送路径优化研究

综合考虑战时物流配送车辆路径问题（VRP）的多目标评价，提出多属性道路网络下战时物流配送的VRP算法，并建立完全分层优化模型。将进化算法与传统优化技术相结合，构造了模型的两层求解算法，第一层采用遗传算法和模拟退火算法混合的GASA算法，第二层采用枚举法。并以成品燃油配送为例进行了实验，结果表明算法较标准遗传算法更有效。     

基于启发式函数蚁群算法的VRP优化研究

车辆路径问题是一个NP难题,蚁群算法是求解诸如车辆路径安排等组合优化问题的有效工具,为此利用启发式函数对传统的蚁群算法进行改进和优化.并通过实例对该方法进行检验,其结果显示,启发式函数蚁群算法的性能,优于传统的蚁群算法.     

基于智能垃圾箱回收阈值的回收车辆路径问题

<正>本文针对智能回收车辆路径问题，考虑了智能垃圾箱回收阈值对回收公司运营成本的影响，构建了回收公司运营成本最小化的优化模型，并运用PSO粒子群算法进行车辆路径优化，并结合实例对模型和算法的有效性进行了验证。结果表明，回收公司通过确定合理的垃圾箱回收阈值进行车辆优化调度，可以有效降低回收运营成本，进而为智慧城市回收物流网络建设提供了理论借鉴。     

基于5.8G ETC的高速路网二义性研究

随着我国高速公路的不断发展，形成了纵横交错、四通八达的高速公路交通网，为公路运输提供了非常多的路径选择。但是由于路网的架构趋于复杂化，二义性路径问题尤其在高速公路发达的省份较为突出。目前的MTC收费车辆采用RFID解决二义性的问题已经在很多省份应用，在这些省份ETC的车辆的二义性问题反而成为一个重要内容。本文采用5.8G ETC技术，记录车辆行驶的路径，可以为实现准确收费和精确拆分提供依据。     

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。     

铁路超限装载方案的运输路径选择研究

为使超限装载方案和运输路径更为安全、经济,针对铁路超限装载方案和运输路径综合优化问题,以超限运输的装载方案优化和运输路径优化为主要目标,同时兼顾运输里程及对既有线路正常运营组织的干扰,以装载方案符合基本要求、路段运输限界、线路通过能力为约束,构建超限装载方案和运输路径的综合优化模型,并根据模型的特点设计遗传-蚁群算法进行求解。最后通过实例验证表明,提出的模型和算法求出的全局最优解,能解决现有分阶段决策方法的最优解为局部最优解的问题。     

基于概率线路图的仓库移动机器人避障路径跟踪研究

为使移动机器人在仓库进行避障行驶,提出了一种基于概率线路图(PRM)优化的避障路径规划跟踪算法。该算法通过采样点数量和连接阀值距离的调整、对地图尺寸扩展等方法对PRM算法进行优化,提高路径规划效率和解决路径安全性不高的问题,同时将优化得到的最优路径作为参数,利用纯追踪轨迹算法进行路径跟踪,取得较好的效果。在matlab软件仿真平台进行仿真,结果表明基于PRM路径优化算法和纯追踪轨迹算法能使移动机器人安全地在仓库中进行避障行驶,所设计的算法具有安全性、有效性和可靠性。     

单车场公交车辆调度问题的研究

根据单车场公交车辆调度优化问题的特点,建立了以车辆总空驶时间最少为第一目标,以所需车辆数最少为第二目标的双目标优化模型。采用遗传算法进行求解。实际应用表明该模型和算法能够快速得到公交车辆调度优化方案的满意解。     

A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows

For VRP with time windows (VRPTW) solved by conventional cluster-first and route-second approach, temporal information is usually considered with vehicle routing but ignored in the process of clustering. We propose an alternative approach based on spatiotemporal partitioning to solving a large-scale VRPTW, considering jointly the temporal and spatial information for vehicle routing. A spatiotemporal representation for the VRPTW is presented that measures the spatiotemporal distance between two customers. The resulting formulation is then solved by a genetic algorithm developed for k-medoid clustering of large-scale customers based on the spatiotemporal distance. The proposed approach showed promise in handling large scale networks.     

Optimizing road network daily maintenance operations with stochastic service and travel times

This paper studies optimization methods for a routing problem encountered in daily maintenance operations of a road network. Stochastic service and travel times on road segments are considered. The problem is formulated as a variation of the capacitated arc routing problem (CARP). A chance-constrained programming model is firstly developed and solved by a branch-and-cut algorithm. A stochastic programming model with recourse is also proposed to take into account the recourse costs in case of route failure. The problem is solved by an adaptive large neighborhood search algorithm. The computational experiments demonstrate the effectiveness of the algorithm.     

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.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion

The green vehicle routing and scheduling problem (GVRSP) aims to minimize green-house gas emissions in logistics systems through better planning of deliveries/pickups made by a fleet of vehicles. We define a new mixed integer liner programming (MIP) model which considers heterogeneous vehicles, time-varying traffic congestion, customer/vehicle time window constraints, the impact of vehicle loads on emissions, and vehicle capacity/range constraints in the GVRSP. The proposed model allows vehicles to stop on arcs, which is shown to reduce emissions up to additional 8% on simulated data. A hybrid algorithm of MIP and iterated neighborhood search is proposed to solve the problem.     

A memetic algorithm for the open capacitated arc routing problem

In this paper, an open capacitated arc routing problem (OCARP) is defined and considered. The OCARP seeks to find a set of minimum-cost open routes that can serve the tasks (i.e., required arcs) of a given graph, subject to the vehicle capacity and travel distance. A mathematical programming formulation and a lower bound are established. An effective memetic algorithm is developed for solving the OCARP. Computational experiments demonstrate that the proposed algorithm can produce high quality solutions within a reasonable computational time span, and the proposed memetic algorithm is superior to the classical genetic algorithm in solution quality.     

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).