考虑时变速度的多车场绿色车辆路径模型及优化算法

针对多车场绿色车辆路径问题,根据顾客的坐标位置,采用K-means聚类方法将顾客分配给不同的车场;考虑时变速度和实时载重对车辆油耗和碳排放的影响,确定车辆油耗和碳排放的度量函数;在此基础上,以车辆油耗成本、碳排放成本、车辆使用成本、驾驶员工资以及时间窗惩罚成本之和最小化作为优化目标,构建多车场绿色车辆路径模型,并根据模...     

考虑交通拥堵规避的低碳时变车辆路径问题研究

针对时变路网条件下的低碳车辆路径问题,首先,分析车辆离散行驶速度与连续行驶时间之间的关系,依据“先进先出”准则设计基于时间段划分的路段行驶时间计算方法,引入考虑车辆速度、实时载重、行驶距离与道路坡度因素的碳排放计算函数;然后,在此基础上以所有车辆的碳排放量最小为目标构建低碳时变车辆路径问题数学模型;最后,引入交通拥堵指数,设计交通拥堵规避方法,并根据模型特点设计一种改进蚁群算法求解.实验结果表明,所提出方法能有效规避交通拥堵、缩短车辆行驶时间、减少车辆碳排放,促进物流配送与生态环境和谐发展.     

A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem

This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the demands of customers are fulfilled by a fleet of fixed number of vehicles with various capacities and related costs. Moreover, the vehicles start at the depot and terminate at one of the customers. This problem is an important variant of the classical vehicle routing problem and can cover more practical situations in transportation and logistics. We propose a multistart adaptive memory programming metaheuristic with modified tabu search algorithm to solve this new vehicle routing problem. The algorithmic efficiency and effectiveness are experimentally evaluated on a set of generated instances.     

Very large-scale vehicle routing: new test problems,algorithms, and results

The standard vehicle routing problem was introduced in the OR/MS literature about 45 years ago. Since then, the vehicle routing problem has attracted an enormous amount of research attention. In the late 1990s, large vehicle routing problem instances with nearly 500 customers were generated and solved using metaheuristics. In this paper, we focus on very large vehicle routing problems. Our contributions are threefold. First, we present problem instances with as many as 1200 customers along with estimated solutions. Second, we introduce the variable-length neighbor list as a tool to reduce the number of unproductive computations. Third, we apply record-to-record travel with a variable-length neighbor list to 32 problem instances and obtain high-quality solutions, very quickly.     

A Road Timetable to aid vehicle routing and scheduling

Both within and between urban conurbations, the time of a journey and the corresponding shortest path in a road network from an origin to a destination may depend on the time of the day, the day of the week and the season of the year. Significant journey time differences occur mainly due to recurring instances and variations in levels of traffic congestion throughout the year. This paper examines the issues involved in constructing a database of road times for a road network that uses time-dependent data on the travel times for individual roads in the network to provide the expected times and distances between locations for journeys starting at different times. The benefits of time-dependent vehicle routing and scheduling systems are demonstrated by using real-world data for the road network in the north west of England.     

基于交通流的车辆路径选择优化方法

针对交通流变化对车辆路径选择的影响, 寻找在交通流变化情况下对出行路线进行规划的方法。采用粒子群算法与动态规划相结合的方法对路径进行优化, 可得各车辆在交通流影响下的优秀路线。基于真实环境中的路网结构和交通数据进行模拟, 结果表明, 该方法可增加路径优化的真实性和动态性, 而且道路交通流及车辆出行时间都对车辆路径选择产生重要影响。     

A new approach on auxiliary vehicle assignment in capacitated location routing problem

The location routing problem (LRP) considers locating depots and vehicle routing decisions simultaneously. In classic LRP the number of customers in each route depends on the capacity of the vehicle. In this paper a capacitated LRP model with auxiliary vehicle assignment is presented in which the length of each route is not restricted by main vehicle capacity. Two kinds of vehicles are considered: main vehicles with higher capacity and fixed cost and auxiliary vehicles with lower capacity and fixed cost. The auxiliary vehicles can be added to the transportation system as an alternative strategy to cover the capacity limitations and they are just used to transfer goods from depots to vehicles and cannot serve the customers by themselves. To show the applicability of the proposed model, some numerical examples derived from the well-known instances are used. Moreover the model has been solved by some meta-heuristics for large sized instances. The results show the efficiency of the proposed model and the solution approach, considering the classic model and the exact solution approach, respectively.     

An exact hybrid method for the vehicle routing problem with time windows and multiple deliverymen

The vehicle routing problem with time windows and multiple deliverymen (VRPTWMD) is a variant of the vehicle routing problem with time windows in which service times at customers depend on the number of deliverymen assigned to the route that serves them. In particular, a larger number of deliverymen in a route leads to shorter service times. Hence, in addition to the usual routing and scheduling decisions, the crew size for each route is also an endogenous decision. This problem is commonly faced by companies that deliver goods to customers located in busy urban areas, a situation that requires nearby customers to be grouped in advance so that the deliverymen can serve all customers in a group during one vehicle stop. Consequently, service times can be relatively long compared to travel times, which complicates serving all scheduled customers during regular work hours. In this paper, we propose a hybrid method for the VRPTWMD, combining a branch-price-and-cut (BPC) algorithm with two metaheuristic approaches. We present a wide variety of computational results showing that the proposed hybrid approach outperforms the BPC algorithm used as standalone method in terms of both solution quality and running time, in some classes of problem instances from the literature. These results indicate the advantages of using specific algorithms to generate good feasible solutions within an exact method.     

考虑交通拥堵的冷链物流城市配送的GVRP研究

针对目前研究冷链物流车辆路径问题多未考虑交通拥堵对运营成本的影响,将道路拥堵因素融入到冷链物流绿色车辆路径（Green Vehicle Routing Problem）优化数学模型中。兼顾经济成本和环境成本,在时变网络下综合考虑冷链物流中车辆管理成本、运输能耗成本、货损成本、制冷成本以及客户需求时间窗的惩罚成本,同时引入运输和制冷过程中产生的碳排放成本,统筹安排车辆路径,使得物流企业整体运营成本最低,更绿色环保。在此基础上根据模型特点设计改进蚁群算法进行求解,用实例对模型和算法进行仿真,验证该模型和方法可以有效地规避拥堵时段,降低配送成本,促进物流企业的节能减排,可以为物流企业冷链配送路径决策提供良好的参考依据。     

Vehicle routing problem with stochastic travel times including soft time windows and service costs

This paper studies a vehicle routing problem with soft time windows and stochastic travel times. A model is developed that considers both transportation costs (total distance traveled, number of vehicles used and drivers' total expected overtime) and service costs (early and late arrivals). We propose a Tabu Search method to solve this model. An initialization algorithm is developed to construct feasible routes by taking into account the travel time stochasticity. Solutions provided by the Tabu Search algorithm are further improved by a post-optimization method. We conduct our computational experiments for well-known problem instances. Results show that our Tabu Search method performs well by obtaining very good final solutions in a reasonable amount of time.     

Minimum cost VRP with time-dependent speed data and congestion charge

A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area.     

基于拥堵指数的改进蜂群算法在DVRP中的应用

城市道路在不同时段、突发情况下的通畅程度存在较大的差异, 对物流成本的影响已不容忽视. 静态车辆路径规划不能反应路况的变化, 因此本文引入分时段的拥堵指数, 以总成本最小为目标函数, 并采用规避高峰期区域的种群初始化算法, 构造出改进的蜂群算法. 实验结果表明, 采用改进蜂群算法进行路径规划, 由于能回避拥堵路段, 因此虽然不是最短的运输路径, 但却能实现更低的总体运输成本.     

The single vehicle routing problem with deliveries and selective pickups

The single vehicle routing problem with deliveries and selective pickups (SVRPDSP) is defined on a graph in which pickup and delivery demands are associated with customer vertices. The difference between this problem and the single vehicle routing problem with pickups and deliveries (SVRPPD) lies in the fact that it is no longer necessary to satisfy all pickup demands. In the SVRPDSP a pickup revenue is associated with each vertex, and the pickup demand at that vertex will be collected only if it is profitable to do so. The net cost of a route is equal to the sum of routing costs, minus the total collected revenue. The aim is to design a vehicle route of minimum net cost, visiting each customer, performing all deliveries, and a subset of the pickups. A mixed integer linear programming formulation is proposed for the SVRPDSP. Classical construction and improvement heuristics, as well as a tabu search heuristic (TS), are developed and tested on a number of instances derived from VRPLIB. Computational results show that the solutions produced by the proposed heuristics are near-optimal. There is also some evidence that the best solutions identified by the heuristics are frequently non-Hamiltonian and may contain one or two customers visited twice.     

移动Ad Hoc网络基于路由协议的拥塞控制

为解决Ad Hoc网络的AODV路由协议在通信过程中存在的拥塞问题,提出了改进AODV路由协议的思想。根据网络链路拥塞度的大小采取不同措施和节点路由,建立不相关多径路由分流以避免拥塞。仿真结果表明,改进后的路由协议有效地减少了发生拥塞的几率,从而提高了移动Ad Hoc网络的性能。     

A note on the truck and trailer routing problem

This study considers the relaxed truck and trailer routing problem (RTTRP), a relaxation of the truck and trailer routing problem (TTRP). TTRP is a variant of the well studied vehicle routing problem (VRP). In TTRP, a fleet of trucks and trailers are used to service a set of customers with known demands. Some customers may be serviced by a truck pulling a trailer, while the others may only be serviced by a single truck. This is the main difference between TTRP and VRP. The number of available trucks and available trailers is limited in the original TTRP but there are no fixed costs associated with the use of trucks or trailers. Therefore, it is reasonable to relax this fleet size constraint to see if it is possible to further reduce the total routing cost (distance). In addition, the resulting RTTRP can also be used to determine a better fleet mix. We developed a simulated annealing heuristic for solving RTTRP and tested it on 21 existing TTRP benchmark problems and 36 newly generated TTRP instances. Computational results indicate that the solutions for RTTRP are generally better than the best solutions in the literature for TTRP. The proposed SA heuristic is able to find better solutions to 18 of the 21 existing benchmark TTRP instances. The solutions for the remaining three problems are tied with the best so far solutions in the literature. For the 36 newly generated problems, the average percentage improvement of RTTRP solutions over TTRP solutions is about 5%. Considering the ever rising crude oil price, even small reduction in the route length is significant.     

多跳Ad Hoc网络基于路由协议的拥塞控制

为解决Ad Hoc网络的DSR路由协议在通信过程中存在拥塞问题,提出了改进DSR路由协议的思想,即联合“最短路径”和节点发送接收数据包时的“传输状态”来避免拥塞。仿真结果表明,改进后的路由协议有效地减少了发生拥塞的几率,从而提高了无线多跳Ad Hoc网络环境下Web通信的性能。     

Heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem in a carrier

In this paper, we present heuristic algorithms for a three-dimensional loading capacitated vehicle routing problem arising in a real-world situation. In this problem, customers make requests of goods, which are packed in a sortment of boxes. The objective is to find minimum cost delivery routes for a set of identical vehicles that, departing from a depot, visit all customers only once and return to the depot. Apart of the usual 3D container loading constraints which ensure that the boxes are packed completely inside the vehicles and that the boxes do not overlap each other in each vehicle, the problem also takes into account constraints related to the vertical stability of the cargo and multi-drop situations. The algorithms are based on the combination of classical heuristics from both vehicle routing and container loading literatures, as well as two metaheuristic strategies, and their use in more elaborate procedures. Although these approaches cannot assure optimal solutions for the respective problems, they are relatively simple, fast enough to solve real instances, flexible enough to include other practical considerations, and normally assure relatively good solutions in acceptable computational times in practice. The approaches are also sufficiently generic to be embedded with algorithms other than those considered in this study, as well as they can be easily adapted to consider other practical constraints, such as the load bearing strength of the boxes, time windows and pickups and deliveries. Computational tests were performed with these methods considering instances based on the vehicle routing literature and actual customers’ orders, as well as instances based on a real-world situation of a Brazilian carrier. The results show that the heuristics are able to produce relatively good solutions for real instances with hundreds of customers and thousands of boxes.     

A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times

The two-dimensional vehicle routing problem (2L-VRP) is a realistic extension of the classical vehicle routing problem in which customers’ demands are composed by sets of non-stackable items. Examples can be found in real-life applications such as the transportation of furniture or industrial machinery. Often, it is necessary to consider stochastic travel times due to traffic conditions or customers availability. However, there is a lack of works discussing stochastic versions of the 2L-VRP. This paper offers a model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime. To solve this stochastic and non-smooth version of the 2L-VRP, a hybrid simheuristic algorithm is proposed. Our approach combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics. Our algorithm is tested on an extensive benchmark, which extends the deterministic one for the 2L-VRP with unrestricted and non-oriented loading.     

基于拥塞预规避控制的QoS路由选择算法研究

现有网络常用的路由算法(比如链路状态路由算法)都不具有拥塞预规避控制能力．提出了一种基于拥塞预规避控制的QoS路由算法(QRCP)．该算法对业务流量进行了更为合理的分配，避免或尽量减少对将来业务流的连接请求的影响，最终减少网络的拥塞概率．通过实验仿真，结果表明，该算法在拥塞避免与网络效益上．比现有的最宽一最短(WSP)与最短．最宽(SWP)路径算法具有明显的优越性．     

ITS和GPS系统下车辆实时调度问题的研究

ITS和GPS技术的发展使得道路交通状况和车辆位置能够被实时监控,从而车辆调度中心能够根据道路交通状况实时调整车辆行驶路线,以避开交通拥挤路段,降低车辆的行驶时间。建立了ITS和GPS系统下车辆实时调度模型,并且就车辆行驶过程中如何降低问题的状态空间进行了研究。