On solving periodic re-optimization dynamic vehicle routing problems

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. One of its important variants is the Dynamic Vehicle Routing Problem (DVRP) in which not all customers are known in advance, but are revealed as the system progresses. DVRP is a Dynamic Optimization Problem (DOP) that has become a challenging research topic in the past two decades. In DOPs, at least one part of the problem changes as time passes. For DVRP, customers change as a system progresses. Consequently, DVRP applications are seen to operate on a dynamic basis in various real-life systems. To date, a time-based evaluation approach has been used to evaluate periodic re-optimized DVRP systems, which are evaluated by solving while using a specific time budget. In this paper, we solve DVRP while using an enhanced Genetic Algorithm (GA) that tries to increase both diversity and the capability to escape from local optima. Also, we propose a fair weighted fitness evaluation approach as an alternative for the biased time-based approach, regardless of the specifications and power of the running system. The proposed enhanced GA outperformed the previously published algorithms based on both the time-based and weighted fitness evaluation approaches.     

A decision support system for optimizing dynamic courier routing operations

In this paper, we propose a prototype of a decision support system (DSS) that integrates a hybrid neighborhood search algorithm to solve the offline and online routing problems arising in courier service. In the dynamic operational environment of courier service, new customer orders and order cancellations continually arrive over time and thus disrupt the optimal routing schedule that was originally designed. This calls for the real-time re-optimization of routes. As service level is sensitive to whether allowable service time intervals are wide or narrow, it is valuable to study how adjustable and flexible time windows influence the courier service efficiency in a dynamic environment. To capture these dynamic features, a dynamic vehicle routing problem (DVRP) that simultaneously considers new customer orders and order cancellations is investigated in this study. Meanwhile, fuzzy time windows are formulated in the DVRP model to quantify the service level and explore the service efficiency. To tackle the new problem, we propose a competitive hybrid neighborhood search heuristic for (re)optimizing the offline and online routes. Numerical computational experiments and the comparison with results from Lingo show that our algorithm is capable of re-optimizing dynamic problems effectively and accurately in a very short time. The proposed model and algorithms are able to enhance courier service level without further expense of a longer traveling distance or a larger number of couriers.     

Dynamic vehicle routing using genetic algorithms

Many difficult combinatorial optimization problems have been modeled as static problems. However, in practice, many problems are dynamic and changing, while some decisions have to be made before all the design data are known. For example, in the Dynamic Vehicle Routing Problem (DVRP), new customer orders appear over time, and new routes must be reconfigured while executing the current solution. Montemanni et al. [1] considered a DVRP as an extension to the standard vehicle routing problem (VRP) by decomposing a DVRP as a sequence of static VRPs, and then solving them with an ant colony system (ACS) algorithm. This paper presents a genetic algorithm (GA) methodology for providing solutions for the DVRP model employed in [1]. The effectiveness of the proposed GA is evaluated using a set of benchmarks found in the literature. Compared with a tabu search approach implemented herein and the aforementioned ACS, the proposed GA methodology performs better in minimizing travel costs. Franklin T. Hanshar is currently a M.Sc. student in the Department of Computing and Information Science at the University of Guelph, Ontario, Canada. He received a B.Sc. degree in Computer Science from Brock University in 2005. His research interests include uncertain reasoning, optimization and evolutionary computation. Beatrice Ombuki-Berman is currently an Associate Professor in the Department of Computer Science at Brock University, Ontario, Canada. She obtained a PhD and ME in Information Engineering from University of The Ryukyus, Okinawa, Japan in 2001 and 1998, respectively. She received a B.Sc. in Mathematics and Computer Science from Jomo Kenyatta University, Nairobi, Kenya. Her primary research interest is evolutionary computation and applied optimization. Other research interests include neural networks, machine learning and ant colony optimization.     

动态车辆路径问题的研究进展及发展趋势

随着智能运输的发展,动态车辆路径问题(Dynamic vehicle routing problem,DVRP)已引起学界的日益关注.分析DVRP的特征,从动态要素的角度将DVRP模型分为基于动态需求的VRP、基于实时交通信息的VRP、基于动态需求和实时交通信息的VRP三种类型,并进行分类综述.在此基础上,对3类DVRP模型的路线更新策略及求解算法的研究进展进行介绍,最后指出DVRP未来的发展趋势.     

求解动态需求车辆调度问题的自适应量子遗传算法

针对物流配送过程中存在的动态车辆调度问题,即带载车量约束的实时优化车辆路径问题,提出一种自适应量子遗传算法,用于最小化配送成本.根据搜索点目标函数的变化率,提出一种自适应量子旋转门更新方式,并通过子种群适应度值的变化确定量子旋转角的方向和大小,进而引导种群进化方向,提高算法的全局搜索广泛性;设计了一种变异操作,用于保持自适应量子遗传算法的种群多样性,进而提高算法全局搜索的宽泛性;引入基于两元素搜索原则的局部搜索方法来增强算法的局部优化能力.仿真实验和算法比较验证了所提算法的有效性和优越性.     

动态车辆路径问题的分区灵活分批TSP 策略

动态车辆路径问题是当前车辆路径问题的新兴热门问题,但其实时优化策略研究仍然有较大的改进空间。鉴于此,在一般分区分批旅行商问题(TSP)策略的基础上,提出了分区灵活分批TSP策略,并对策略有效性进行了分析。最后进行了实例仿真验证,结果表明,所提出策略能够减少车辆服务顾客的平均行驶距离,从而减少顾客的平均系统时间。     

An improved LNS algorithm for real-time vehicle routing problem with time windows

This paper studies the dynamic vehicle routing problem with hard time windows (DVRPTW). The main study course of this problem was briefly reviewed. The solving strategy and algorithm of the problem are put forward. First of all, DVRPTW problem is decomposed into a series of static VRPTW. When and how to decompose the DVRP is the issue, that must be addressed. An event-trigger mechanism has been proposed and used to decompose the DVRPTW into a series of system delay-snapshots. The trigger event to be adopted is a new request arrival during the stable operation. And each snapshot is regarded as a static VRPTW. Whether each static VRPTW can quickly and efficiently be solved within a given time or a shorter time, i.e. the solving time is another issue for the DVRPTW. In the solving process, how to merge the latest requirement to the current solution is the third issue that must be solved. An improved large neighborhood search (LNS) algorithm is proposed to solve the static problem. Utilizing the remove-reinsert process of the LNS, the latest request nodes are regarded as a part of the removed nodes; these nodes can be inserted into the original or current solution in good time in the reinsertion process; meanwhile, its computing speed is high and effective for it does not need to resolve primal problem each time. Computational results of a large number of test problems, which cited from Solomon's static benchmarks and Lacker’s dynamic data set, show that our method is superior to other methods in most instances.     

Ad Hoc网络中基于DSR的QoS路由协议研究

AdHoc网络的特性决定为各种多媒体业务的服务质量提供保证是很难解决的问题。AdHoc网络路由协议的QoS研究正是试图解决这样的问题。本文介绍了DSR路由协议和AdHoc网络QoS路由技术的相关概念。然后,对基于DSR的QoS实现的路由协议进行了详细的分析。最后探讨了其今后的发展动态和研究方向。     

自组织覆盖网络QoS组播动态路由优化研究

针对覆盖组播节点的动态特性,研究自组织覆盖网络带度和延时约束的组播动态路由问题,提出了动态覆盖组播路由算法AHMQ。组播树由目的节点驱动动态渐近形成,动态路由优化在通信过程中进行。协议是软状态的,仅要求节点维护局部状态信息,同时利用覆盖网络技术和无线媒质的广播能力,降低了网络负载,提高了重构能力。对算法进行了分析研究,通过实验验证了该算法具有较好的性能。     

基于重填化学反应优化解决无线网络动态最短选路优化问题

采用人工智能优化技巧轻易解决静态最短选路(SP)优化问题,但是随着无线通讯的发展,诸如移动Ad Hoc网络与无线传感网络等新式无线网络被大量广泛使用.在这些新式无线网络中,网络拓扑随着时间而不断变化从而导致最短选路优化问题被转变成动态优化问题.提出了一种新式的基于化学反应优化(CRO)的算法来解决这个问题.化学反应优化...     

一种新的与线网顺序无关的随机优化总体布线算法

针对目前总体布线中仍然存在的3个关键问题;布线结果受布线顺序的影响、总体布线图中拥挤区域的不可预见性、线网连接式样受到算法的限制等,该文提出了一种新的不受线网顺序影响的总体布线算法,并实现了相应的总体布线器RINO－Router。该算法采用随机优化方法来保 证先后被拆线重布的线网有相同的通过拥挤区域的机会,并能得到GRG边的拥挤度估计值;采用高效的Steiner树改造算法构造避开拥挤区域的布线树,采用典型电路实例进行了测试,并将布线结果与基于多商品流算法的总体布线器Matula-Router进行了对比。结果表明,RINO－Router能够在短得多的运行时间内求得质量与Matula-Router相近的总体布线解。     

An object-oriented evaluation framework for dynamic vehicle routing problems under real-time information

The dynamic vehicle routing problems (DVRP) is an extension of vehicle routing problems (VRP) in order to consider possible variations of travel times in the network. In this research, a two-stage framework for solving dynamic vehicle routing problem is proposed. In the first stage, the sweep method is adopted in vehicle assignment. In the second stage, a tabu search algorithm is implemented to improve routes under real-time information. The framework is implemented in an object-oriented approach and possible benefit from real-time information is illustrated through numerical simulation. The simulation-assignment model, DynaTAIWAN is applied in numerical simulation to evaluate real-time routing strategies in a traffic network. Numerical experiments are conducted in a 50 Nodes Network and a Taichung City. The results show that positive benefits could be achieved through utilization of real-time information with careful design.     

改进变邻域搜索算法求解动态车辆路径问题

针对动态车辆路径问题DVRP(Dynamic Vehicle Routing Problem)的优化问题,提出一种改进算法。该算法在分析路径寻优问题的局部特性的基础上,利用变邻域搜索算法VNS(Variable Neighbourhood Search)对路径空间进行"局部探索",结合变异机制对路径空间进行"全局开采",最后根据近邻优先原则将动态路径片段安插到适宜的路径中。实验结果验证了算法的有效性。     

Solving the truck and trailer routing problem based on a simulated annealing heuristic

In this study, we consider the application of a simulated annealing (SA) heuristic to the truck and trailer routing problem (TTRP), a variant of the vehicle routing problem (VRP). In the TTRP, some customers can be serviced by either a complete vehicle (that is, a truck pulling a trailer) or a single truck, while others can only be serviced by a single truck for various reasons. SA has seen widespread applications to various combinatorial optimization problems, including the VRP. However, to our best knowledge, it has not been applied to the TTRP. So far, all the best known results for benchmark TTRP instances were obtained using tabu search (TS). We applied SA to the TTRP and obtained 17 best solutions to the 21 benchmark TTRP benchmark problems, including 11 new best solutions. Moreover, the computational time required by the proposed SA heuristic is less than those reported in prior studies. The results suggest that SA is competitive with TS on solving the TTRP.     

Scatter search for the stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries

In parallel with the growth of both domestic and international economies, there have been substantial efforts in making manufacturing and service industries more environmental friendly (i.e., promotion of environmental protection). Today manufacturers have become much more concerned with coordinating the operations of manufacturing (for new products) and recycling (for reuse of resources) together with scheduling the forward/reverse flows of goods over a supply chain network. The stochastic travel-time vehicle routing problem with simultaneous pick-ups and deliveries (STT-VRPSPD) is one of the major operations problems in bi-directional supply chain research. The STT-VRPSPD is a very challenging and difficult combinatorial optimization problem due to many reasons such as a non-monotonic increase or decrease of vehicle capacity and the stochasticity of travel times. In this paper, we develop a new scatter search (SS) approach for the STT-VRPSPD by incorporating a new chance-constrained programming method. A generic genetic algorithm (GA) approach for STT-VRPSPD is also developed and used as a reference for performance comparison. The Dethloff data will be used to evaluate the performance characteristics of both SS and GA approaches. The computational results suggest that the SS solutions are superior to the GA solutions.     

基于两阶段求解算法的动态车辆调度问题研究

在分析需求动态变化的基础上,根据需求信息的提出顺序,将动态配送问题转换成不同时刻的静态车辆调度问题,建立基于时间轴的动态车辆调度模型;利用量子理论改进遗传算法,设计量子遗传算法;针对动态车辆调度问题实时性强的特点,设计＂初始优化阶段＋实时优化阶段＂的两阶段求解策略,通过信息更新插入动态需求客户,并对已产生的计划路径进行局部优化调整.通过仿真计算,验证了模型和算法的有效性.     

Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands

This paper considers an advanced capacitated location routing problem in a distribution network with multiple pickup and delivery routes, and each customer placing random multi-item demands on it. The pickup and delivery services need two fleets of vehicles and will form two different sets of routes. However, the unpredictability of variation in the multi-item demands makes the routing of multi-compartment vehicles to accommodate such demands complex. To solve this multifaceted problem, a new process employing the TABU search is proposed in this research study. This proposed approach includes three stages: location selection, customer assignment, and vehicle routing. The innovative concept is to divide all customers into assignment-determined and assignment-undetermined groups in order to narrow down the search area of a solution domain so the TABU search can be more efficient and effective. Two sets of benchmarks are then generated to verify the quality of the proposed method. According to the experiment results, the proposed solution process can both resolve the problems and yield good results in a reasonable amount of computing time. The analysis of the solution process parameters is also provided. In addition, the comparisons between stochastic demand and deterministic demand cases are calculated and discussed as well.     

基于节点密度加权的T-LEACH三维动态路由协议研究

随着无线传感网络在三维动态环境应用需求的俱增,如何在动态拓扑的三维网络环境下,设计能量高效和数据传输高可靠性的路由协议是当前学术界的研究热点。现有的三维路由协议未充分考虑节点移动和环境因素的影响,多为二维静态协议的补充和支持。将拓扑结构和地理结构的路由协议相结合,提出了基于节点密度加权的T-LEACH三维动态路由协议。使用T-LEACH算法获得全局节点密度信息,DDRS算法预知路由空洞和使用DDRS-R的恢复算法逃逸空洞和修正路由,有效地实现规避局部最优问题和迅速逃逸路由空洞的目标,提高了网络整体的健壮性和生存时间。通过在课题组设计的无线传感器网络三维环境路由协议仿真平台上的实验和对比,证明本文提出的路由协议在能量消耗,网络生存期及数据交付率等方面优于现有协议,具有良好的应用前景。     

The SR-GCWS hybrid algorithm for solving the capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) is a well known problem which has long been tackled by researchers for several decades now, not only because of its potential applications but also due to the fact that CVRP can be used to test the efficiency of new algorithms and optimization methods. The objective of our work is to present SR-GCWS, a hybrid algorithm that combines a CVRP classical heuristic with Monte Carlo simulation using state-of-the-art random number generators. The resulting algorithm is tested against some well-known benchmarks. In most cases, our approach is able to compete or even outperform much more complex algorithms, which is especially interesting if we consider that our algorithm does not require any previous parameter fine-tuning or set-up process. Moreover, our algorithm has been able to produce high-quality solutions almost in real-time for most tested instances. Another important feature of the algorithm worth mentioning is that it uses a randomized constructive heuristic, capable of generating hundreds or even thousands of alternative solutions with different properties. These alternative solutions, in turn, can be really useful for decision-makers in order to satisfy their utility functions, which are usually unknown by the modeler. The presented methodology may be a fine framework for the development of similar algorithms for other complex combinatorial problems in the routing arena as well as in some other research fields.     

基于量子遗传算法的路由选择

网络中存在许多设计和优化问题,其中相当一部分属于NP类型。传统的解法由于计算复杂度过大而失效。文中探讨了该类问题中路由选择问题的一种新的解决方法:量子遗传算法。就路由选择问题的数学模型进行了简单的介绍,并深入研究了量子遗传算法及其在路由选择优化问题中的应用,最后在计算机上进行了模拟分析实验。仿真实验的结果表明,量子遗传算法在性能上优于常规遗传算法。该算法搜索速度快、效率高,并且具有较强的实用性和鲁棒性。