Heuristic methods for delay constrained least cost routing using /spl kappa/-shortest-paths

The delay constrained least cost (DCLC) problem is to find the least cost path in a graph subject to a delay constraint. First formulated in the context of routing in computer networks, the DCLC model also applies to problems of path planning and other decision problems. DCLC is NP-complete. Many heuristic methods have been proposed for it. This note presents two new methods based on the /spl kappa/-shortest-path (/spl kappa/SP) approach. These heuristic methods-one centralized, the other distributed-are both polynomial. In numerical experiments the proposed algorithms almost always find the optimal paths.     

基于MPH的时延约束Steiner树算法

为了在时延约束务件下进一步优化组播树代价,并降低算法计算复杂度,研究了时延受限的Steiner树问题.分析了MPH(minimum path heuristic)算法的计算复杂度;在此基础上设计了一个时延约束Steiner树算法DCMPH(delay-constrained MPH)用于构造时延约束最小代价组播树.该算法中每个目的结点通过与当前组播树有最小代价的路径加入组播树;若时延不满足要求,则通过合并最小时延SPT(shortest path tree)树进而产生一个满足时延约束的最小代价组播树.仿真实验表明,DCMPH算法生成的组播树在保证时延要求的情况下,与同类算法相比取得了很好的代价性能和较低的计算复杂度.     

基于选择函数的分布式启发算法DCLC—DSF

QoS路由的DCLC单播路由（Delay-Constrained Least-Cost Unicast Routing）问题属于NP－完全问题。本文提出一种多项式复杂度的分布式启发算法DCLC－DSF。DCLC－DSF基于简单的选择函数,每个网络结点只需维持本地的状态信息：相邻链路的延时和代价度量。该算法有以下优点：1）简单性;2）动态性;3）重路由功能;4）协商功能。在最坏情况下,DCLC－DSF的消息复杂度为O（e^2）,结点的计算复杂度为O（n^2）;在稳定的网络环境下,消息复杂度为O（e）。此外,本文还给出DCLC－DSF算法有限状态机模型。仿真实验表明：DCLC－DSF算法的平均代价不精确度是最佳算法的5－8％,证明它是一种简单、精确、健壮的的启发式算法。     

A heuristic method for the vehicle routing problem with backhauls and inventory

The purpose of this paper is to determine the route of the vehicle routing problem with backhauls (VRPB), delivering new items and picking up the reused items or wastes, and resolve the inventory control decision problem simultaneously since the regular VRPB does not. Both the vehicle routing decision for delivery and pickup, and the inventory control decision affect each other and must be considered together. Hence, a mathematical model of vehicle routing problem with backhauls and inventory (VRPBI) is proposed. Since finding the optimal solution(s) for VRPBI is a NP-hard problem, this paper proposes a heuristic method, variable neighborhood tabu search (VNTS), adopting six neighborhood searching approaches to obtain the optimal solution. Moreover, this paper compares the proposed heuristic method with two other existing heuristic methods. The experimental results indicate that the proposed method is better than the two other methods in terms of average logistic cost (transportation cost and inventory cost).     

A hybrid scatter search meta-heuristic for delay-constrained multicast routing problems

This paper investigates the first hybrid scatter search and path relinking meta-heuristic for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. The underpinning mathematic model of the DCLC multicast routing problem is the constrained Steiner tree problem in graphs, a well known NP-complete problem. After combining a path relinking method as the solution combination method in scatter search, we further explore two improvement strategies: tabu search and variable neighborhood search, to intensify the search in the hybrid scatter search algorithm. A large number of simulations on some benchmark instances from the OR-library and a group of random graphs of different characteristics demonstrate that the improvement strategy greatly affects the performance of the proposed scatter search algorithm. The hybrid scatter search algorithm intensified by a variable neighborhood descent search is highly efficient in solving the DCLC multicast routing problem in comparison with other algorithms and heuristics in the literature.     

Efficient hierarchical QoS routing in ATM networks

For reducing network information to achieve scalability in large ATM networks, ATM Private Network-to-Network Interface (PNNI) adopts hierarchical routing. Consequently, although routing complexity is significantly reduced, numerous issues in PNNI routing require further study to achieve more efficient, accurate, scalable, and QoS-aware routing.Several methods are adopted herein to achieve efficient, scalable, and QoS-aware ATM PNNI routing. First, an efficient aggregation scheme, referred to as Asymmetric Simple, is proposed. The aggregated routing information includes available bandwidth, delay and cost. Second, two approaches for defining link costs are investigated, namely, the Markov Decision Process (MDP) approach and the Competitive On-Line (COL) routing approach, and these are compared with the Widest Path (WP) approach. Finally, a dynamic update policy, referred to as the dynamic cost-based update (DCU) policy, is proposed to improve the accuracy of the aggregated information and the performance of hierarchical routing, while decreasing the frequency of re-aggregation and information distribution.Simulation results demonstrate that the proposed Asymmetric Simple aggregation scheme yields very good network utilization while significantly reducing the amount of advertised information. Between these two link cost functions, the MDP approach provides a systematic method of defining call admission function and yields better network utilization than the COL approach. The proposed DCU policy also yields an enhanced network utilization while significantly reducing the frequency of re-aggregation and the amount of distributed aggregation information.     

基于划分的蚁群算法求解货物权重车辆路径问题

考虑单产品分销网络中的车辆路径问题(VRP:vehicle routing problem).与以往诸多研究不同的是,建立了一种带货物载重量的VRP模型(weighted VRP),即车辆在两个顾客之间行驶时的载重量也作为影响运输费用的一个因素考虑.因此,需求量较大的顾客拥有较高的车辆运输优先权.在分析了问题性质的基础上,提出一种基于划分策略的蚁群算法PMMAS求解货物权重车辆路径问题,并与其他常用的启发式算法进行比较分析,表明了算法的有效性.     

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

The vehicle routing problem （VRP） is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups （VRPSPDP） is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.     

Scoped Bellman-Ford Geographic Routing for Large Dynamic Wireless Sensor Networks

Routing is a fundamental problem in wireless sensor networks. Most previous routing protocols are challenged when used in large dynamic networks as they suffer from either poor scalability or the void problem. In this paper,we propose a new geographic routing protocol,SBFR(Scoped Bellman-Ford Routing),for large dynamic wireless sensor networks. The basic idea is that each node keeps a view scope of the network by computing distance vectors using the distributed BellmanFord method,and maintains a cost for routing to the sink. When forwarding a packet,a node picks the node with minimum cost in its routing table as a temporary landmark. While achieving good scalability,it also solves the void problem in an efficient manner through the combination of Bellman-Ford routing and cost-based geographic routing. Analytical and simulation results show that SBFR outperforms other routing protocols not only because of its robustness and scalability but also its practicality and simplicity.     

An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

In this paper, we present an efficient variable neighborhood search heuristic for the capacitated vehicle routing problem. The objective is to design least cost routes for a fleet of identically capacitated vehicles to service geographically scattered customers with known demands. The variable neighborhood search procedure is used to guide a set of standard improvement heuristics. In addition, a strategy reminiscent of the guided local search metaheuristic is used to help escape local minima. The developed solution method is specifically aimed at solving very large scale real-life vehicle routing problems. To speed up the method and cut down memory usage, new implementation concepts are used. Computational experiments on 32 existing large scale benchmarks, as well as on 20 new very large scale problem instances, demonstrate that the proposed method is fast, competitive and able to find high-quality solutions for problem instances with up to 20,000 customers within reasonable CPU times.     

DCLC路由的选择函数法DCLC-SF

QoS路由的DCLC(Delay-Constrained Least-Cost Routing)路由问题是一个NP--完全问题。本文提出了一种多项式复杂度的启发式算法DCLC-SF(Delay-Constrained Least-Cost Routing Based on Selective Function），DCLC-SF算法基于简单的选择函数，属于源路由算法，算法最坏情况的计算复杂度为O（3ne)。仿真实验证明DCLC-SF算法是一种精确的启发式算法。     

Valiant load-balanced robust routing algorithm for multi-granularity connection requests in traffic-grooming WDM mesh networks

The paper considers the problem of establishing robust routes for multi-granularity connection requests in traffic-grooming WDM mesh networks and proposes a novel Valiant load-balanced robust routing scheme for the hose uncertain model. Our objective is to minimize the total network cost when construct the stable virtual topology that assure robust routing for all possible multi-granularity connection requests under the hose model. Since the optimization problem is shown to be NP-complete, two heuristic algorithms are proposed and evaluated. Finally we compare the traffic throughput of the virtual topology by Valiant load-balanced robust routing scheme with that of the traditional traffic-grooming algorithm under the same total network cost by computer simulation.     

Joint gateway selection,transmission slot assignment,routing and power control for wireless mesh networks

Wireless mesh networks (WMNs) provide cost effective solutions for setting up a communications network over a certain geographic area. In this paper, we study strategic problems of WMNs such as selecting the gateway nodes along with several operational problems such as routing, power control, and transmission slot assignment. Under the assumptions of the physical interference model and the tree-based routing restriction for traffic flow, a mixed integer linear programming (MILP) formulation is presented, in which the objective is to maximize the minimum service level provided at the nodes. A set of valid inequalities is derived and added to the model in an attempt to improve the solution quality. Since the MILP formulation becomes computationally infeasible for larger instances, we propose a heuristic method that is aimed at solving the problem in two stages. In the first stage, we devise a simple MILP problem that is concerned only with the selection of gateway nodes. In the second stage, the MILP problem in the original formulation is solved by fixing the gateway nodes from the first stage. Computational experiments are provided to evaluate the proposed models and the heuristic method.     

基于OpenFlow的自适应QoS流量控制方法设计与实现

随着新型业务类型如视频会议、网络游戏、交互应用等不断涌现,如何利用有限的网络资源进行有效的流量控制,以保障业务的服务质量(Quality of Service,QoS)已成为一个非常迫切的问题。而目前已有的QoS流量控制方法大多存在着对网络资源的利用率低、可靠性差、粒度粗、实现困难,可扩展性差等问题。软件定义网络(Software Defined Network,SDN)提出的控制层与数据层分离思想,为解决此类问题提供了崭新的思路。本文提出了一种基于OpenFlow技术的QoS流量控制方法,利用自适应多约束QoS路由技术,提高了QoS控制的灵活性与可靠性,实现了对网络资源的高效利用和业务流控制的细粒度。最后,我们在OpenvSwitch环境下验证了该方法的有效性。     

Variable Neighborhood Search heuristic for the Inventory Routing Problem in fuel delivery

In this paper we observe the extension of the vehicle routing problem (VRP) in fuel delivery that includes petrol stations inventory management and which can be classified as the Inventory Routing Problem (IRP) in fuel delivery. The objective of the IRP is to minimize the total cost of vehicle routing and inventory management. We developed a Variable Neighborhood Search (VNS) heuristic for solving a multi-product multi-period IRP in fuel delivery with multi-compartment homogeneous vehicles, and deterministic consumption that varies with each petrol station and each fuel type. The stochastic VNS heuristic is compared to a Mixed Integer Linear Programming (MILP) model and the deterministic “compartment transfer” (CT) heuristic. For three different scale problems, with different vehicle types, the developed VNS heuristic outperforms the deterministic CT heuristic. Also, for the smallest scale problem instances, the developed VNS was capable of obtaining the near optimal and optimal solutions (the MILP model was able to solve only the smallest scale problem instances).     

并行量子遗传算法在QoS组播路由中的应用

随着网络通信技术的发展和Internet的普及,性能出色的组播路由越来越重要。著名的组播路由Steiner树问题是NP完全问题,应采用启发式方法求解。文中在常规量子遗传算法中引入并行进化模型,提出了一种解决多约束QoS组播路由优化问题的算法。在满足带宽、时延约束条件下寻找代价最小的组播树,并合理安排节点负荷,减少通信开销。仿真实验结果表明本算法搜索速度快、全局寻优能力强,性能和效率优于常规量子遗传算法。     

Simulation-based policy generation using large-scale Markovdecision processes

This paper presents a new problem-solving approach, termed simulation-based policy generation (SPG), that is able to generate solutions to problems that may otherwise be computationally intractable. The SPG method uses a simulation of the original problem to create an approximating Markov decision process (MDP) model which is then solved via traditional MDP solution approaches. Since this approximating MDP is a fairly rich and robust sequential optimization model, solution policies can be created which represent an intelligent and structured search of the policy space. An important feature of the SPG approach is its adaptive nature, in that it uses the original simulation model to generate improved aggregation schemes, allowing the approach to be applied in situations where the underlying problem structure is largely unknown. In order to illustrate the performance of the SPG methodology, we apply it to a common but computationally complex problem of inventory control, and we briefly discuss its application to a large-scale telephone network routing problem     

QoS multicast routing using a quantum-behaved particle swarm optimization algorithm

QoS multicast routing in networks is a very important research issue in networks and distributed systems. It is also a challenging and hard problem for high-performance networks of the next generation. Due to its NP-completeness, many heuristic methods have been employed to solve the problem. This paper proposes the modified quantum-behaved particle swarm optimization (QPSO) method for QoS multicast routing. In the proposed method, QoS multicast routing is converted into an integer programming problem with QoS constraints and is solved by the QPSO algorithm combined with loop deletion operation. The QPSO-based routing method, along with the routing algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA), is tested on randomly generated network topologies for the purpose of performance evaluation. The simulation results show the efficiency of the proposed method on QoS the routing problem and its superiority to the methods based on PSO and GA.     

Infrastructure deployment and optimization of fog network based on MicroDC and LRPON integration

This paper investigates the issue of infrastructure deployment and optimization (IDO) for Fog network. The Fog network is a new networking paradigm for distributed Micro data center (MicroDC) by using long-reach passive optical network (LRPON) to support delay-sensitive bandwidth-intensive residential, enterprise, and wireless backhaul services. The IDO problem aims at achieving a cost-effective Fog network design in which the deployment cost, power awareness, optical link degradation factors and resource of fog devices are jointly considered in a single optimization framework. An efficient heuristic algorithm named Fast Backward Linking (FBL) is developed to obtain a near-optimal solution for the large scale Fog network. Numerical analyses have validated the feasibility and scalability of the proposed FBL algorithm and the experiment results demonstrate that FBL can significantly outperform Gurobi in terms of efficiency.     

A Hypergraph Based Approach to Declustering Problems

Parallelizing I/O operations via effective declustering of data is becoming essential to scale up the performance of parallel databases or high performance systems. Declustering has been shown to be a NP-complete problem in some contexts. Some heuristic methods have been proposed to solve this problem. However, most methods are not effective in several cases such as queries with different access frequencies or data with different sizes. In this paper, we propose a hypergraph model to formulate the declustering problem. Several interesting theoretical results are achieved by analyzing the proposed model. The proposed approach will allow modeling a wide range of declustering problems. Furthermore, the hypergraph declustering model is used as the basis to develop new heuristic methods, including a greedy method and a hybrid declustering method. Experiments show that the proposed methods can achieve better performance than several declustering methods.