Reliable Ad Hoc Routing Based on Mobility Prediction

Reliability is a very important issue in Mobile Ad hoc NETworks (MANETs). Shortest paths are usually used to route packets in MANETs. However, a shortest path may fail quickly, because some of the wireless links along a shortest path may be broken shortly after the path is established due to mobility of mobile nodes. Rediscovering routes may result in substantial data loss and message exchange overhead. In this paper, we study reliable ad hoc routing in the urban environment. Specifically, we formulate and study two optimization problems. In the minimum Cost Duration-bounded Path (CDP) routing problem, we seek a minimum cost source to destination path with duration no less than a given threshold. In the maximum Duration Cost-bounded Path (DCP) routing problem, we seek a maximum duration source to destination path with cost no greater than a given threshold. We use a waypoint graph to model the working area of a MANET and present an offline algorithm to compute a duration prediction table for the given waypoint graph. An entry in the duration prediction table contains the guaranteed worst-case duration of the corresponding wireless link. We then present an efficient algorithm which computes a minimum cost duration-bounded path, using the information provided in the duration prediction table. We also present a heuristic algorithm for the DCP routing problem. In addition, we show that the proposed prediction and routing schemes can be easily applied for designing reliable ad hoc routing protocols. Simulation results show that our mobility prediction based routing algorithms lead to higher network throughput and longer average path duration, compared with the shortest path routing. This research was supported in part by ARO grant W911NF-04-1-0385 and NSF grant CCF-0431167. The information reported here does not reflect the position or the policy of the federal government.     

Finding disjoint paths with related path costs

We consider routing in survivable networks that provide protection against node or link failures. In these networks resilience against failures is provided by routing connections on pairs of disjoint paths called primary and backup paths. The primary path of a connection carries its traffic under normal circumstances and in the eventuality of a network failure effecting the primary path the connection traffic (all or some portion of it) is rerouted over its backup path. In an online setting as connection requests arrive a pair of disjoint primary and backup paths of least total cost (under some link cost metric) are selected to route the connections. In many situations the cost metric used for the primary path differs from the cost metric used for the backup path. Also in many realistic settings these two cost metrics are related to each other. In this paper we study the problem of finding a pair of edge or node disjoint paths of least total cost where the cost of the primary path is the total cost of its links while the cost for the backup path is α times the sum of the cost of its links, for some given α < 1. We show that the problem is hard to approximate to within a factor for any positive . In addition we show that the problem is complete for a set of hard to approximate problems. On the positive side we show that a simple algorithm achieves an approximation ratio of for the problem.     

A Genetic Algorithm for the Weight Setting Problem in OSPF Routing

With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks.     

Traffic engineering and congestion control for open shortest path first networks

Open shortest path first (OSPF) is the most widely used intra-domain Internet routing protocol. The OSPF protocol directs the Internet traffic along the shortest paths that are defined by the links weight. Traffic engineering is responsible for improving the network performance, for instance, the objective function that minimizes the maximal link utilization which is usually adopted to avoid the network congestion. This paper formulates the model of OSPF routing, and further evaluates the performance of different approaches. We also propose a heuristic algorithm to solve the routing problems by optimizing OSPF weights. Computational results indicated that the proposed algorithm could lead to good load balancing, and make efficient utilization of network resources.     

The Steiner cycle and path cover problem on interval graphs

The Steiner path problem is a common generalization of the Steiner tree and the Hamiltonian path problem, in which we have to decide if for a given graph there exists a path visiting a fixed set of terminals. In the Steiner cycle problem we look for a cycle visiting all terminals instead of a path. The Steiner path cover problem is an optimization variant of the Steiner path problem generalizing the path cover problem, in which one has to cover all terminals with a minimum number of paths. We study those problems for the special class of interval graphs. We present linear time algorithms for both the Steiner path cover problem and the Steiner cycle problem on interval graphs given as endpoint sorted lists. The main contribution is a lemma showing that backward steps to non-Steiner intervals are never necessary. Furthermore, we show how to integrate this modification to the deferred-query technique of Chang et al. to obtain the linear running times.

     

高校专利技术产业化路径选择研究

针对我国高校专利技术高产出与低转化的矛盾,从科技创新资源优化配置与技术创新各环节有效衔接的视角,分析高校专利技术产业化路径的基本类型及特点,提出其影响因素,深入探讨关键因素状态不同时的路径选择。基于此,构建高校专利技术产业化路径选择矩阵与动态转化模型,为我国高校专利技术产业化路径优选提供理论支持与决策参考。     

Two Variations of the Minimum Steiner Problem

Given a set S of starting vertices and a set T of terminating vertices in a graph G = (V,E) with non-negative weights on edges, the minimum Steiner network problem is to find a subgraph of G with the minimum total edge weight. In such a subgraph, we require that for each vertex s S and t T, there is a path from s to a terminating vertex as well as a path from a starting vertex to t. This problem can easily be proven NP-hard. For solving the minimum Steiner network problem, we first present an algorithm that runs in time and space that both are polynomial in n with constant degrees, but exponential in |S|+|T|, where n is the number of vertices in G. Then we present an algorithm that uses space that is quadratic in n and runs in time that is polynomial in n with a degree O(max {max {|S|,|T|}–2,min {|S|,|T|}–1}). In spite of this degree, we prove that the number of Steiner vertices in our solution can be as large as |S|+|T|–2. Our algorithm can enumerate all possible optimal solutions. The input graph G can either be undirected or directed acyclic. We also give a linear time algorithm for the special case when min {|S|,|T|} = 1 and max {|S|,|T|} = 2.The minimum union paths problem is similar to the minimum Steiner network problem except that we are given a set H of hitting vertices in G in addition to the sets of starting and terminating vertices. We want to find a subgraph of G with the minimum total edge weight such that the conditions required by the minimum Steiner network problem are satisfied as well as the condition that every hitting vertex is on a path from a starting vertex to a terminating vertex. Furthermore, G must be directed acyclic. For solving the minimum union paths problem, we also present algorithms that have a time and space tradeoff similar to algorithms for the minimum Steiner network problem. We also give a linear time algorithm for the special case when |S| = 1, |T| = 1 and |H| = 2.An extended abstract of part of this paper appears in Hsu et al. (1996).Supported in part by the National Science Foundation under Grants CCR-9309743 and INT-9207212, and by the Office of Naval Research under Grant No. N00014-93-1-0272.Supported in part by the National Science Council, Taiwan, ROC, under Grant No. NSC-83-0408-E-001-021.     

An investigation into the vehicle routing problem with time windows and link capacity constraints

In this work, we investigate a new, yet practical, variant of the vehicle routing problem called the vehicle routing problem with time windows and link capacity constraints (VRPTWLC). The problem considers new constraints imposed on road links with regard to vehicle passing tonnage, which is motivated by a business project with a Hong Kong transportation company that transports hazardous materials (hazmats) across the city and between Hong Kong and mainland China. In order to solve this computationally challenging problem, we develop a tabu search heuristic with an adaptive penalty mechanism (TSAP) to help manage the company's vehicle fleet. A new data set and its generation scheme are also presented to help validate our solutions. Extensive computational experiments are conducted, showing the effectiveness of the proposed solution approach.     

Optimization of loss-balanced multicast in all-optical WDM networks

In wavelength-division multiplexing (WDM) optical networks, multicast is implemented by constructing a light-forest, which is a set of light-trees with each light-tree rooted from the multicast source and terminated at a partition subset of the destination nodes. Multicast routing scenario has considerable impact on the quality of optical signal received at each destination. To guarantee the fairness of signal quality at different destinations in a multicast session, it is desirable to construct a loss-balanced light-forest to deliver the multicast traffic. A loss-balanced light-forest is composed of a set of light-trees bounded in size (number of destinations per multicast tree), in size variation (difference in the number of destinations among different multicast trees), and in dimension (maximum source-to-destination distance on each multicast tree). This paper investigates the multicast routing and wavelength assignment (MC-RWA) problem under the loss-balance constraint. The problem is formulated as an optimization model using integer linear programming (ILP). Numerical solutions to the optimization model can supply useful performance benchmarks for loss-balance-constrained optical multicast in WDM networks. This work was supported by the NSF under Grant OCI-0225642 and by the U.S. DoE under Grant DE-FG02–03ER25566.     

Best routes selection in multimodal networks using multi-objective genetic algorithm

In this study, we propose a bi-level multi-objective Taguchi genetic algorithm for a multimodal routing problem with time windows. The mathematic model is constructed, which is featured by two optimal objectives, multiple available transportation manners and different demanded delivery times. After thoroughly analyzing the characteristics of the formulated model, a corresponding bi-level multi-objective Taguchi genetic algorithm is designed to find the Pareto-optimal front. At the upper level, a genetic multi-objective algorithm simultaneously searches the Pareto-optimal front and provides the most feasible routing path choices for the lower level. After generalizing the matrices of costs and time in a multimodal transportation network, the \(k\) -shortest path algorithm is applied to providing some potential feasible paths. A multi-objective genetic algorithm is proposed at the lower level to determine the local optimal combination of transportation manners for these potential feasible paths. To make the genetic algorithm more robust, sounder and faster, the Taguchi (orthogonal) experimental design method is adopted in generating the initial population and the crossover operator. The case study shows that the proposed algorithm can effectively find the Pareto-optimal front solutions and offer series of transportation routes with best combinations of transportation manners. The shipper can easily select the required shipping schemes with specified demands.     

有模糊时间窗的车辆调度组合干扰管理研究

研究带有模糊时间窗的车辆调度组合干扰管理模型及其混合遗传算法.采用时间窗模糊化处理方法,定义客户满意度函数,根据干扰管理思想对车辆调度中组合性干扰事件进行分析,从配送路径、配送成本和客户满意度三个方面进行干扰辨识与度量,建立基于模糊时间窗的车辆调度组合干扰管理模型;构造模型求解的混合遗传算法,将最佳客户插入规则与遗传算法结合,同时在算法中嵌入模糊优化程序以处理问题的模糊特征;进行数值实验,实验结果验证了模型与算法的有效性.     

Finding paths with minimum shared edges

Motivated by a security problem in geographic information systems, we study the following graph theoretical problem: given a graph G, two special nodes s and t in G, and a number k, find k paths from s to t in G so as to minimize the number of edges shared among the paths. This is a generalization of the well-known disjoint paths problem. While disjoint paths can be computed efficiently, we show that finding paths with minimum shared edges is NP-hard. Moreover, we show that it is even hard to approximate the minimum number of shared edges within a factor of $2^{\log^{1-\varepsilon}n}$ , for any constant ε>0. On the positive side, we show that there exists a (k?1)-approximation algorithm for the problem, using an adaption of a network flow algorithm. We design some heuristics to improve the quality of the output, and provide empirical results.     

Capacitated vehicle routing problem on line with unsplittable demands

Journal of Combinatorial Optimization - In this paper we study the capacitated vehicle routing problem. An instance of capacitated vehicle routing problem consists of a set of vertices with demands...     

Models and algorithms for the design of survivable multicommodity flow networks with general failure scenarios

We consider the design of a multicommodity flow network, in which point-to-point demands are routed across the network subject to link capacity restrictions. Such a design must build enough capacity and diverse routing paths through the network to ensure that feasible multicommodity flows continue to exist, even when components of the network fail. In this paper, we examine several methodologies to optimally design a minimum-cost survivable network that continues to support a multicommodity flow under any of a given set of failure scenarios, where each failure scenario consists of the simultaneous failure of multiple arcs. We begin by providing a single extensive form mixed-integer programming formulation for this problem, along with a Benders decomposition algorithm as an alternative to the extensive form approach. We next investigate strategies to improve the performance of the algorithm by augmenting the master problem with several valid inequalities such as cover constraints, connectivity constraints, and path constraints. For the smallest instances (eight nodes, 10 origin–destination pairs, and 10 failure scenarios), the Benders implementation consumes only 10% of the time required by the mixed-integer programming formulation, and our best augmentation strategy reduces the solution time by another 50%. For medium- and large-sized instances, the extensive form problem fails to terminate within 2 h on any instance, while our decomposition algorithms provide optimal solutions on all but two problem instances.     

Minimal path decomposition of complete bipartite graphs

This paper deals with the subject of minimal path decomposition of complete bipartite graphs. A path decomposition of a graph is a decomposition of it into simple paths such that every edge appears in exactly one path. If the number of paths is the minimum possible, the path decomposition is called minimal. Algorithms that derive such decompositions are presented, along with their proof of correctness, for the three out of the four possible cases of a complete bipartite graph.     

带时间窗装卸货问题的改进多策略分组编码遗传算法

通过研究求解PDPTW的分组编码遗传算法（GGA）及多策略分组编码遗传算法（MSGGA），改进了GGA中的交叉算子及MSGGA中的路径调整策略，提出了易位组合交叉算子、单车路径重排策略及需求对换策略。求解了400个客户点的标准算例集，其中4个算例lc2_4_3、lrc1_4_1、lrc2_4_2和lrc2_4_3的行驶总路程有所减少。     

Ein Optimierungsmodell für die wöchentliche Tourenplanung unter Einbeziehung der EU-Sozialvorschriften

The Regulation (EC) No 561/2006 and the Directive 2002/15/EC concern the driving and working hours as well as breaks and rest periods of drivers in road transportation. Although the regulations have an enormous effect on vehicle routing and scheduling, only parts of them have been integrated in few solution approaches and some vehicle routing models so far. This paper starts with the presentation of the restrictions of the relevant European Community regulations. Then, a mixed integer linear programming model for the vehicle routing problem with time windows including all rules of the regulations for a planning horizon of an entire week is presented. The model is solved with CPLEX and the impact of the regulations on the resulting vehicle schedules is analyzed by means of computational experiments.     

A pragmatic algorithm for the train-set routing: The case of Korea high-speed railway

This paper presents a two-phased train-set routing algorithm to cover a weekly train timetable with minimal working days of a minimal number of train-sets. First, relax maintenance requirements and obtain minimum cost routes by solving the polynomial relaxation. Then, maintenance-feasible routes are generated from the crossovers of the minimum cost routes. This pragmatic approach seems particularly effective for the high-speed railway systems, where the railway topology is relatively simple with few end stations while the trains are frequent. Applied to the current weekly timetable of the Korea high-speed railway, we could find an optimal feasible routing, which is an 8.8% improvement over the current routing generated by a set partitioning approach based on a path generation scheme.     

OMEGa: an optimistic most energy gain method for minimum energy multicasting in wireless ad hoc networks

In wireless ad hoc networks where every device runs on its own battery, the energy consumption is critical to lengthen the network lifetime. The communication among devices in the network can be categorized as unicasting and multicasting (including broadcasting). For the case of unicasting, computing the energy optimal path between the two communicating nodes is polynomially solvable by computing the shortest path. But for the case of multicasting, shortest path or minimum spanning tree does not guarantee an energy optimal communication. In this paper, we present our novel approach, Optimistic Most Energy Gain (OMEGa) method, for the minimum energy multicasting in wireless ad hoc networks. OMEGa aims at maximum utilization of Wireless Multicast Advantage (WMA), which essentially means covering more nodes by using larger energy. Both theoretical and experimental analysis shows OMEGa method performs very well. Research is partially supported by NSF and Air Force grants.     

考虑路径可行性与仓储集货模式下的回收车辆路径问题研究

逆向物流回收车辆调度过程中,往往出现由于需求节点位置及需求量信息的不确定性导致难以合理决策完成回收任务所需派出回收车辆的数目,此时,第三方物流逐渐被应用于回收产品的运输服务中。然而在实际的回收过程中,通常各物流需求节点的需求量较小,需要对多个物流节点的产品集中后统一进行处理;同时由于外界因素的限制,不能保证任意两个节点间均存在可行路径,需要通过中转运输的方式寻找替代路线。针对以上问题,本文提出一种基于路径可行性与仓储集货运输模式的回收车辆路径设计方案,并根据问题的特点对传统蚁群算法（ACO）中编码方式以及概率选择操作方式进行改进,提出一种逆选择操作蚁群算法（ACO-nso）。最后通过算例证明提出模型与算法的有效性。