14,222 research outputs found

    An Improved Algorithm for Fixed-Hub Single Allocation Problem

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    This paper discusses the fixed-hub single allocation problem (FHSAP). In this problem, a network consists of hub nodes and terminal nodes. Hubs are fixed and fully connected; each terminal node is connected to a single hub which routes all its traffic. The goal is to minimize the cost of routing the traffic in the network. In this paper, we propose a linear programming (LP)-based rounding algorithm. The algorithm is based on two ideas. First, we modify the LP relaxation formulation introduced in Ernst and Krishnamoorthy (1996, 1999) by incorporating a set of validity constraints. Then, after obtaining a fractional solution to the LP relaxation, we make use of a geometric rounding algorithm to obtain an integral solution. We show that by incorporating the validity constraints, the strengthened LP often provides much tighter upper bounds than the previous methods with a little more computational effort, and the solution obtained often has a much smaller gap with the optimal solution. We also formulate a robust version of the FHSAP and show that it can guard against data uncertainty with little cost

    Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU

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    A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses binary and integer encoding and genetic operators adapted to this problem. Our GA is improved by generated initial solution with hubs located at middle nodes. The obtained experimental results are compared with the best known solutions on all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own randomly generated instances up to 6000 nodes. Our approach outperforms most well-known heuristics in terms of solution quality and time execution and it allows hitherto unsolved problems to be solved

    Energy-Delay Tradeoff and Dynamic Sleep Switching for Bluetooth-Like Body-Area Sensor Networks

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    Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people's health. This paper considers a system scenario relevant to such applications, where a smart-phone acts as a data-collecting hub, gathering data from a number of wireless-capable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors' energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless body-area sensor network at some desired point close to the optimal energy-delay tradeoff curve. This tradeoff curve is a function of the employed physical-layer protocol: in particular, it depends on the multiple-access scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widely-used Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energy-delay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control "NULL" packets necessary to maintain the connection alive, have a non-zero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.Comment: Extended version (with proofs details in the Appendix) of a paper accepted for publication on the IEEE Transactions on Communication

    Location models for airline hubs behaving as M/D/c queues

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    Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value α\alpha. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.Hub location, congestion, tabu-search

    Algoritmos de aproximação para problemas de localização e alocação de terminais

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    Orientador: Lehilton Lelis Chaves PedrosaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: No Problema de Localização e Alocação de Terminais, a entrada é um espaço métrico composto por clientes, localidades e um conjunto de pares de clientes; uma solução é um subconjunto das localidades, onde serão abertos terminais, e uma atribuição de cada par de clientes a uma rota, que começa no primeiro cliente, passando em um ou dois terminais, e terminando no segundo cliente. O objetivo é encontrar uma solução que minimize o tamanho de todas as rotas somado com o custo de abertura de terminais. Os algoritmos de aproximação da literatura consideram apenas o caso em que o conjunto de terminais abertos é dado como parte da entrada, e o problema se torna atribuir clientes aos terminais; ou então quando o espaço é definido em classes especiais de grafos. Neste trabalho, apresentamos o primeiro algoritmo de aproximação com fator constante para o problema de, simultaneamente, escolher localidades para abrir terminais e atribuir clientes a estes. A primeira parte desta dissertação cria algoritmos de aproximação para diversas variantes do problema. A estratégia principal é reduzir os problemas de localização e alocação de terminais aos problemas clássicos de localidades, como o problema de localização de instalações e o problema das k-medianas. A redução transforma uma instância de localização e alocação de terminais em uma instância de um destes problemas, que então é resolvida usando algoritmos de aproximação já existentes na literatura. A saída do algoritmo induz uma solução para o problema original, com uma perda constante no fator de aproximação. Na segunda parte, o foco é o Problema de Localização e Alocação Única de Terminais (SAHLP), que é uma variação em que cada cliente deve estar conectado a apenas um terminal, além de não haver limite na quantidade de terminais abertos. A principal contribuição é um algoritmo 2.48-aproximado para o SAHLP, baseado em arredondamento de uma nova formulação de programa linear para o problema. O algoritmo é composto por duas fases: na primeira, a solução fracionária é escalada e um subconjunto de terminais é aberto, e na segunda, atribuímos clientes aos terminais abertos. A primeira fase segue o formato padrão de filtering para problemas de localidades. A segunda, no entanto, exigiu o desenvolvimento de novas ideias e é baseada em múltiplos critérios para realizar a atribuição. A principal técnica atribui cada cliente ao terminal aberto mais próximo, se este estiver em sua vizinhança; caso contrário, o cliente se conecta ao terminal que melhor balanceia múltiplos custos, relacionados à distância entre elesAbstract: In the Hub Location Problem (HLP), the input is a metric space composed of clients, locations and a set of pairs of clients; a solution is a subset of locations to open hubs and an assignment for each pair of clients to a route starting in the first client, passing through one or two hubs and ending in the second client. The objective is to find a solution that minimizes the length of all routes plus the cost of opening hubs. The currently known approximation algorithms consider only the case in which the set of hubs is given as part of the input and the problem is assigning clients to hubs; or when the space is defined on special classes of graphs. In this work, we present the first constant-factor approximation algorithms for the problem of, simultaneously, selecting hubs and allocating clients. The first part of the thesis derives approximation algorithms for several variants of the problem. The main strategy is to reduce the hub location problems to classical location problems, such as Facility Location and k-Median. The reduction transforms an instance of hub location into an instance of a corresponding location problem, which is then solved by known approximation algorithm. The algorithm¿s output induces a solution of the original problem within a constant loss in the approximation ratio. In the second part, we focus on the Single Allocation Hub Location Problem (SAHLP), that is the variant in which a client must be connected to only one hub and there is no limit on the number of open hubs. Our main contribution is a 2.48-approximation algorithm for the SAHLP, based on the rounding of a new linear programming formulation. The algorithm is composed of two phases: in the first one, we scale the fractional solution and open a subset of hub locations, and in the second one, we assign clients to open hubs. The first phase follows the standard filtering framework for location problems. The latter, however, demanded the development of new ideas and is based on a multiple criteria assignment. The main technique is assigning a client to a closest open hub only if there are near open hubs, and otherwise selecting the hub which balances multiple costsMestradoCiência da ComputaçãoMestre em Ciência da Computação2016/12006-1CAPESFAPES

    An Electromagnetism Metaheuristic for the Uncapacitated Multiple Allocation Hub Location Problem

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    In this article, the results achieved by applying an electromagnetism (EM) inspired metaheuristic to the uncapacitated multiple allocation hub location problem (UMAHLP) are discussed. An appropriate objective function which natively conform with the problem, 1-swap local search and scaling technique conduce to good overall performance.Computational tests demonstrate the reliability of this method, since the EM-inspired metaheuristic reaches all optimal/best known solutions for UMAHLP, except one, in a reasonable time.This research was partially supported by the Serbian Ministry of Education and Science under project 174010

    A goal programming methodology for multiobjective optimization of distributed energy hubs operation

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    This paper addresses the problem of optimal energy flow management in multicarrier energy networks in the presence of interconnected energy hubs. The overall problem is here formalized by a nonlinear constrained multiobjective optimization problem and solved by a goal attainment based methodology. The application of this solution approach allows the analyst to identify the optimal operation state of the distributed energy hubs which ensures an effective and reliable operation of the multicarrier energy network in spite of large variations of load demands and energy prices. Simulation results obtained on the 30 bus IEEE test network are presented and discussed in order to demonstrate the significance and the validity of the proposed method
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