移位交换网的最优路由算法

移位交换网是重要的互联网络之一 ,在并行计算中有着广泛应用 .然而 ,它缺少任意点对间的最短路由算法 .已有的路由算法都不能保证其任意节点对间都是最短路由 .文中给出了一个最短路由算法 ,也是最优路由算法 ,它使得从源节点到目的节点的任何信息都是沿最短路由传输 .同时 ,我们还得到了任意节点对间的距离公式     

An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem

The well-known Shortest Path problem (SP) consists in finding a shortest path from a source to a destination such that the total cost is minimized. The SP models practical and theoretical problems. However, several shortest path applications rely on uncertain data. The Robust Shortest Path problem (RSP) is a generalization of SP. In the former, the cost of each arc is defined by an interval of possible values for the arc cost. The objective is to minimize the maximum relative regret of the path from the source to the destination. This problem is known as the minmax relative regret RSP and it is NP-Hard. We propose a mixed integer linear programming formulation for this problem. The CPLEX branch-and-bound algorithm based on this formulation is able to find optimal solutions for all instances with 100 nodes, and has an average gap of 17 % on the instances with up to 1,500 nodes. We also develop heuristics with emphasis on providing efficient and scalable methods for solving large instances for the minmax relative regret RSP, based on Pilot method and random-key genetic algorithms. To the best of our knowledge, this is the first work to propose a linear formulation, an exact algorithm and metaheuristics for the minmax relative regret RSP.     

Routing optimization in packet switching communication networks

For routing assignments a special model and an optimization algorithm are proposed. The efficiency of the routing assignments is evaluated by the average value of the total cost of delays for all packets in the network. It is the objective function. The main idea is that traffic, which is transmitted from the source node to the destination node, can be split between two or more logical paths. The minimum of the objective function can be found by varying the traffic on every path and simultaneously from all the source nodes to the destination nodes. If this approach is applied, then the objective function is nonseparable and nonlinear. Because its shape is unknown in advance, an adaptive nonlinear optimization algorithm is proposed. For evaluating its efficiency a special set of test functions has been used.     

满足路径约束的最优路算法

满足路径约束的最优路问题已被证明是NP-hard问题。本针对源点到宿点满足两个QoS(服务质量)度量的路由问题，给出一种保证时延的最小费用路由启发式算法。这个算法的优点是计算较简单、占用内存小、时间短。算法的复杂度是多项式的，表明算法是有效的。     

Optimal design of hierarchical networks with free main path extremes

We propose an optimal, two-stage procedure for the optimal design of minimum cost hierarchical spanning networks, consisting of a main path and secondary trees. The optimal location of the origin and destination nodes of the path is also found. We test our procedure and compare it with a known method.     

Large margin shortest path routing

A new discriminative approach to routing inspired by the large margin criterion serving as a basis for support vector machines is presented. The proposed formulation uses the benefit of the dualization convex program, and it is possible for standard solvers to learn the weighting metrics of the shortest path routing. In order to demonstrate this and due to its simplicity, the single path flow allocation problem is considered in this article. It was found that the weight settings performed within a few percent of that of the optimal general routing where the flow for each demand was optimally distributed over all paths between the source and destination.     

Fuzzy Linear Programming Approach for Solving Fuzzy Transportation Problems with Transshipment

To find the fuzzy optimal solution of fuzzy transportation problems it is assumed that the direct route between a source and a destination is a minimum-cost route. However, in actual application, the minimum-cost route is not known a priori. In fact, the minimum-cost route from one source to another destination may well pass through another source first. In this paper, a new method is proposed to find the fuzzy optimal solution of fuzzy transportation problems with the following transshipment: (1) From a source to any another source, (2) from a destination to another destination, and (3) from a destination to any source. In the proposed method all the parameters are represented by trapezoidal fuzzy numbers. To illustrate the proposed method a fuzzy transportation problem with transshipment is solved. The proposed method is easy to understand and to apply for finding the fuzzy optimal solution of fuzzy transportation problems with transshipment occurring in real life situations.     

Evaluation model of problem solving

Problem solving is a style of thinking, which transforms a given problem to the goal state through a so-called PS (problem solving) path. Different from the traditional GPS (General Problem Solver) approach, the focus in this paper is placed on how to judge the performance of PS paths, that is, the evaluation problem of problem solving.A series of PS paths point from the given source problem to the destination goal, then form a PSN (PS Network). This paper proposes an elaborated CPSN (Coordinate Problem Solving Network) as the evaluation model of problem solving. In CPSN, each problem is assigned a unique coordinate and then each PS path can have an evaluation vector. Several examples show such arrangement can give more insight to PS paths.Furthermore, an incremental learning algorithm is developed for the update of CPSN. When a new PS path is obtained, it is not necessary to recalculate the whole CPSN. Examples show such algorithm provides a more efficient way in finding new PS paths.     

Single source unsplittable flows with arc-wise lower and upper bounds

Mathematical Programming - In a digraph with a source and several destination nodes with associated demands, an unsplittable flow routes each demand along a single path from the common source to...     

Dynamic journeying under uncertainty

We introduce a journey planning problem in multi-modal transportation networks under uncertainty. The goal is to find a journey, possibly involving transfers between different transport services, from a given origin to a given destination within a specified time horizon. Due to uncertainty in travel times, the arrival times of transport services at public transport stops are modeled as random variables. If a transfer between two services is rendered unsuccessful, the commuter has to reconsider the remaining path to the destination. The problem is modeled as a Markov decision process in which states are defined as paths in the transport network. The main contribution is a backward induction method that generates an optimal policy for traversing the public transport network in terms of maximizing the probability of reaching the destination in time. By assuming history independence and independence of successful transfers between services we obtain approximate methods for the same problem. Analysis and numerical experiments suggest that while solving the path dependent model requires the enumeration of all paths from the origin to the destination, the proposed approximations may be useful for practical purposes due to their computational simplicity. In addition to on-time arrival probability, we show how travel and overdue costs can be taken into account, making the model applicable to freight transportation problems.     

An Algorithm for Solving the Shortest Path Improvement Problem on Rooted Trees Under Unit Hamming Distance

Shortest path problems play important roles in computer science, communication networks, and transportation networks. In a shortest path improvement problem under unit Hamming distance, an edge-weighted graph with a set of source–terminal pairs is given. The objective is to modify the weights of the edges at a minimum cost under unit Hamming distance such that the modified distances of the shortest paths between some given sources and terminals are upper bounded by the given values. As the shortest path improvement problem is NP-hard, it is meaningful to analyze the complexity of the shortest path improvement problem defined on rooted trees with one common source. We first present a preprocessing algorithm to normalize the problem. We then present the proofs of some properties of the optimal solutions to the problem. A dynamic programming algorithm is proposed for the problem, and its time complexity is analyzed. A comparison of the computational experiments of the dynamic programming algorithm and MATLAB functions shows that the algorithm is efficient although its worst-case complexity is exponential time.     

Shortest path in the presence of obstacles: An application to ocean shipping

This paper presents the problem of determining the estimated time of arrival (ETA) at the destination port for a ship located at sea. This problem is formulated as a shortest path problem with obstacles, where the obstacles are modelled by polygons representing the coastlines. An efficient solution algorithm is proposed to solve the problem. Instead of generating a complete visibility graph and solving the problem as an ordinary shortest path problem, the algorithm constructs arcs to the ship node during the solution process only when needed. This greatly enhances the algorithmic performance. Computational results based on test problems from an actual dry-bulk shipping operation are provided. The proposed algorithm is implemented in a decision support system for the planning of ship operations and it has successfully been applied on several real life problems.     

Ranking paths in stochastic time-dependent networks

In this paper we address optimal routing problems in networks where travel times are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. Nevertheless, in some particular cases an origin–destination path must be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is an NP-hard problem.     

Some optimal path problems subject to improvements

In this paper, the functional equation technique of dynamic programming is applied to solve the problems of a) determining an optimal path from a given origin to a fixed destination when the path is subject to a given number of improvements, b) finding an optimal path from a given origin to an assigned destination by passing at least once through each node of a set of specified nodes when the path is subject to a given number of improvements, c) obtaining an optimal path from a given origin to a fixed destination by passing at least once through at least one node of each ofK sets of specified nodes when the path is subject to a given number of improvements.

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Zusammenfassung In dieser Arbeit wird die Funktionalgleichung des dynamischen Programmierens verwendet, um folgende drei Netzwerkprobleme zu lösen: a) Bestimmung eines optimalen Pfades von einem gegebenen Anfangs- zu einem gegebenen Endknoten, wenn längs einem Pfad eine gewisse Anzahl von Verbesserungen möglich sind. b) Wie a), wobei zusätzlich der Pfad mindestens einmal durch jeden Knoten einer spezifizierten Knotenmenge gehen soll. c) Wie a), wobei der Pfad durch mindestens einen Knoten in jeder vonK spezifizierten Knotenmenge gehen soll.

     

Rectilinear shortest paths in the presence of rectangular barriers

In this paper we address the following shortest-path problem. Given a point in the plane andn disjoint isothetic rectangles (barriers), we want to construct a shortestL ₁ path (not crossing any of the barriers) from the source point to any given query point. A restricted version of this problem (where the source and destination points are knowna priori) had been solved earlier inO(n ²) time. Our approach consists of preprocessing the source point and the barriers to obtain a planar subdivision where a query point can be located and a shortest path connecting it to the source point quickly transvered. By showing that any such path is monotone in at least one ofx ory directions, we are able to apply a plane sweep technique to divide the plane intoO(n) rectangular regions. This leads to an algorithm whose complexity isO(n logn) preprocessing time,O(n) space, andO(logn+k) query time, wherek is the number of turns on the reported path. If only the length of the path is sought,O(logn) query time suffices. Furthermore, we show an (n logn) time lower bound for the case where the source and destination points are known in advance, which implies the optimality of our algorithm in this case.A preliminary version of this paper appeared in theProceedings of the First Symposium on Computational Geometry (1985).Supported in part by CNPq-Conselho Nacional de Desenvolvimento Científico e Tecnológico (Brazil).Supported in part by the National Science Foundation under Grants MCS 8420814 and ECS 8340031.     

Algorithms for an Integer Multicommodity Network Flow Problem with Node Reliability Considerations

In this paper, we consider the problem of sending a set of multiple commodities from their origin to destination nodes via intermediate hubs. Each hub node is associated with a reliability function, which depends on the total flow that crosses that hub. The probability that each commodity is successfully relayed from its origin to its destination is given by the product of hub reliabilities on the commodity’s path. The problem we consider seeks to find minimum-cost commodity paths such that each commodity reaches its destination with a sufficiently large probability. We first formulate the problem as a nonlinear multicommodity network-flow problem and prove that it is strongly NP-hard. We then present two linearization techniques for this formulation, and propose a pair of lower- and upper-bounding formulations, which can then be used within an exact cutting-plane algorithm to solve the problem. Finally, we analyze the computational effectiveness of our proposed strategies on a set of randomly generated instances.     

On finding dissimilar Pareto-optimal paths

The aim of the present paper is to provide a methodology for finding a set of alternative paths between an origin and a destination site on which routing one or a set of dangerous goods. Finding a set of paths allows one to equally distribute the total risk among the population exposed. The concept of equity of risk is here related to the concept of determining spatially dissimilar paths. We divide our approach into two phases. In the first phase we find a set of Pareto-Optimal paths between an origin and a destination, by implementing a multicriteria shortest path algorithm. In the second one, for each path previously found, and by using a geographical information system, we construct a Buffer Zone approximating the impact area of a material being released after an accident. Based on these Buffer Zones, a dissimilarity index between every pair of paths can be derived in order to find the most spatially different routes. We then compare our method with an iterative penalty method and discuss computational results based both on a real application and on test problems.     

Optimum departure times for commuters in congested networks

We propose an algorithm to compute the optimum departure time and path for a commuter in a congested network. Constant costs for use of arcs, cost functions of travel time depending on exogenous congestion and schedule delay are taken into account. A best path for a given departure time is computed with a previous algorithm for the generalized shortest path problem. The globally optimal departure time and an optimal path are determined by adapting Piyavskii's algorithm to the case of one-sided Lipschitz functions.This research has benefited from a grant of the Transportation Center of Northwestern University. The first author's research was partially supported by NSF grant No. SES-8911517 to Northwestern University. The second author's research was partially supported by AFOSR grants No. 89-0512 and 90-0008 to Rutgers University.