A dual ascent framework for Lagrangean decomposition of combinatorial problems

We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers

A dual ascent framework for Lagrangean decomposition of combinatorial problems

We propose a general dual ascent framework for Lagrangean decomposition of combinatorial problems. Although methods of this type have shown their efficiency for a number of problems, so far there was no general algorithm applicable to multiple problem types. In this work, we propose such a general algorithm. It depends on several parameters, which can be used to optimize its performance in each particular setting. We demonstrate efficacy of our method on graph matching and multicut problems, where it outperforms state-of-the-art solvers including those based on subgradient optimization and off-the-shelf linear programming solvers

Distributed object recognition in Visual Sensor Networks

This work focuses on Visual Sensor Networks (VSNs) which perform visual analysis tasks such as object recognition. There, the goal is to find the image in a reference database which is the closest match to the image captured by camera sensor nodes. Recognition is performed by relying on visual features extracted from the acquired image, which are matched against a database of labeled features in order to find the closest image match. The matching functionalities are often implemented at a central controller outside the VSN. In contrast, we study the performance trade-offs involved in distributing the matching functionalities inside the VSN by letting sensor nodes performing parts of the matching process. We propose an optimization framework to optimally distribute the matching task to in-network sensor nodes with the goal of minimizing the overall completion time of the recognition task. The proposed optimization framework is then used to assess the performance of distributed matching, comparing it to a traditional, centralized approach in realistic VSN scenarios

Object Replication Algorithms for World Wide Web

Object replication is a well-known technique to improve the accessibility of the Web sites. It generally offers reduced client latencies and increases a site's availability. However, applying replication techniques is not trivial and a large number of heuristics have been proposed to decide the number of replicas of an object and their placement in a distributed web server system. This paper presents three object placement and replication algorithms. The first two heuristics are centralized in the sense that a central site determines the number of replicas and their placement. Due to the dynamic nature of the Internet traffic and the rapid change in the access pattern of the World-Wide Web, we also propose a distributed algorithm where each site relies on some locally collected information to decide what objects should be replicated at that site. The performance of the proposed algorithms is evaluated through a simulation study. Also, the performance of the proposed algorithms has been compared with that of three other well-known algorithms and the results are presented. The simulation results demonstrate the effectiveness and superiority of the proposed algorithms

The Multicast Policy and Its Relationship to Replicated Data Placement

In this paper we consider the communication complexity of maintaining the replicas of a logical data-item, in a database distributed over a computer network. We propose a new method, called the minimum spanning tree write, by which a processor in the network should multicast a write of a logical data-item, to all the processors that store replicas of the item. Then we show that the minimum spanning tree write is optimal from the communication cost point of view. We also demonstrate that the method by which a write is multicast to all the replicas of a data-item, affects the optimal replication scheme of the item, i.e., at which processors in the network the replicas should be located. Therefore, next we consider the problem of determining an optimal replication scheme for a data item, assuming that each processor employs the minimum spanning tree write at run-time. The problem for general networks is shown NP-Complete, but we provide efficient algorithms to obtain an optimal allocation scheme for three common types of network topologies. They are completely-connected, tree, and ring networks. For these topologies, efficient algorithms are also provided for the case in which reliability considerations dictate a minimum number of replicas

Valid Inequalities and Facets of the Capacitated Plant Location Problem

Recently, several successful applications of strong cutting plane methods to combinatorial optimization problems have renewed interest in cutting plane methods, and polyhedral characterizations, of integer programming problems.In this paper, we investigate the polyhedral structure of the capacitated plant location problem. Our purpose is to identify facets and valid inequalities for a wide range of capacitated fixed charge problems that contain this prototype problem as a substructure.The first part of the paper introduces a family of facets for a version of the capacitated plant location problem with constant capacity K for all plants. These facet inequalities depend on K and thus differ fundamentally from the valid inequalities for the uncapacitated version of the problem. We also introduce a second formulation for a model with indivisible customer demand and show that it is equivalent to a vertex packing problem on a derived graph. We identify facets and valid inequalities for this version of the problem by applying known results for the vertex packing polytope

Developing service supply chains by using agent based simulation

The Master thesis present a novel approach to model a service supply chain with agent based simulation. Also, the case study of thesis is related to healthcare services and research problem includes facility location of healthcare centers in Vaasa region by considering the demand, resource units and service quality. Geographical information system is utilized for locating population, agent based simulation for patients and their illness status probability, and discrete event simulation for healthcare services modelling. Health centers are located on predefined sites based on managers’ preference, then each patient based on the distance to health centers, move to the nearest point for receiving the healthcare services. For evaluating cost and services condition, various key performance indicators have defined in the modelling such as Number of patient in queue, patients waiting time, resource utilization, and number of patients ratio yielded by different of inflow and outflow. Healthcare managers would be able to experiment different scenarios based on changing number of resource units or location of healthcare centers, and subsequently evaluate the results without necessity of implementation in real life.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Online balancing two independent criteria

We study the online bicriteria load balancing problem in this paper. We choose a system of distributed homogeneous file servers located in a cluster as the scenario and propose two online approximate algorithms for balancing their loads and required storage spaces. We first revisit the best existing solution for document placement, and rewrite it in our first algorithm by imposing some flexibilities. The second algorithm bounds the load and storage space of each server by less than three times of their trivial lower bounds, respectively; and more importantly, for each server, the value of at least one parameter is far from its worst case. The time complexities for both algorithm are O(logM). © 2008 Springer Berlin Heidelberg

File Allocation and Join Site Selection Problem in Distributed Database Systems.

There are two important problems associated with the design of distributed database systems. One is the file allocation problem, and the other is the query optimization problem. In this research a methodology that considers both these aspects is developed that determines the optimal location of files and join sites for given queries simultaneously. Using this methodology, three different mixed integer programming models that describe three cases of the file allocation and join site selection problem are developed. Dual-based procedures are developed for each of the three mixed integer programming models. Extensive computational testing is performed which shows that the dual-based algorithms developed are able to generate solutions which are very close to the optimal. Also, these near optimal solutions are found very quickly, even for large scale problems