Pricing Algorithms For a Two-sided Internet Advertisement Market

The Google AdSense Program is a successful internet advertisement program where Google places contextual adverts on third-party websites and shares the resulting revenue with each publisher. Advertisers have budgets and bid on ad slots while publishers set reserve prices for the ad slots on their websites. Following previous modelling efforts, we model the program as a two-sided market with advertisers on one side and publishers on the other. We show a reduction from the Generalised Assignment Problem (GAP) to the problem of computing the revenue maximising allocation and pricing of publisher slots under a first-price auction. GAP is APX-hard but a (1-1/e) approximation is known. We compute truthful and revenue-maximizing prices and allocation of ad slots to advertisers under a second-price auction. The auctioneer's revenue is within (1-1/e) second-price optimal.

Tracking a Large Number of Objects from Multiple Views

We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NP-hard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and our technique to recover from occlusions. We successfully track hundreds of flying bats and provide an analysis of their group behavior based on 150 reconstructed 3D trajectories.

Approche efficace pour la conception des architectures multiprocesseurs sur puce électronique

Les systèmes multiprocesseurs sur puce électronique (On-Chip Multiprocessor [OCM]) sont considérés comme les meilleures structures pour occuper l'espace disponible sur les circuits intégrés actuels. Dans nos travaux, nous nous intéressons à un modèle architectural, appelé architecture isométrique de systèmes multiprocesseurs sur puce, qui permet d'évaluer, de prédire et d'optimiser les systèmes OCM en misant sur une organisation efficace des nœuds (processeurs et mémoires), et à des méthodologies qui permettent d'utiliser efficacement ces architectures. Dans la première partie de la thèse, nous nous intéressons à la topologie du modèle et nous proposons une architecture qui permet d'utiliser efficacement et massivement les mémoires sur la puce. Les processeurs et les mémoires sont organisés selon une approche isométrique qui consiste à rapprocher les données des processus plutôt que d'optimiser les transferts entre les processeurs et les mémoires disposés de manière conventionnelle. L'architecture est un modèle maillé en trois dimensions. La disposition des unités sur ce modèle est inspirée de la structure cristalline du chlorure de sodium (NaCl), où chaque processeur peut accéder à six mémoires à la fois et où chaque mémoire peut communiquer avec autant de processeurs à la fois. Dans la deuxième partie de notre travail, nous nous intéressons à une méthodologie de décomposition où le nombre de nœuds du modèle est idéal et peut être déterminé à partir d'une spécification matricielle de l'application qui est traitée par le modèle proposé. Sachant que la performance d'un modèle dépend de la quantité de flot de données échangées entre ses unités, en l'occurrence leur nombre, et notre but étant de garantir une bonne performance de calcul en fonction de l'application traitée, nous proposons de trouver le nombre idéal de processeurs et de mémoires du système à construire. Aussi, considérons-nous la décomposition de la spécification du modèle à construire ou de l'application à traiter en fonction de l'équilibre de charge des unités. Nous proposons ainsi une approche de décomposition sur trois points : la transformation de la spécification ou de l'application en une matrice d'incidence dont les éléments sont les flots de données entre les processus et les données, une nouvelle méthodologie basée sur le problème de la formation des cellules (Cell Formation Problem [CFP]), et un équilibre de charge de processus dans les processeurs et de données dans les mémoires. Dans la troisième partie, toujours dans le souci de concevoir un système efficace et performant, nous nous intéressons à l'affectation des processeurs et des mémoires par une méthodologie en deux étapes. Dans un premier temps, nous affectons des unités aux nœuds du système, considéré ici comme un graphe non orienté, et dans un deuxième temps, nous affectons des valeurs aux arcs de ce graphe. Pour l'affectation, nous proposons une modélisation des applications décomposées en utilisant une approche matricielle et l'utilisation du problème d'affectation quadratique (Quadratic Assignment Problem [QAP]). Pour l'affectation de valeurs aux arcs, nous proposons une approche de perturbation graduelle, afin de chercher la meilleure combinaison du coût de l'affectation, ceci en respectant certains paramètres comme la température, la dissipation de chaleur, la consommation d'énergie et la surface occupée par la puce. Le but ultime de ce travail est de proposer aux architectes de systèmes multiprocesseurs sur puce une méthodologie non traditionnelle et un outil systématique et efficace d'aide à la conception dès la phase de la spécification fonctionnelle du système.

Robustness in partial pole placement

The robustness of state feedback solutions to the problem of partial pole placement obtained by a new projection procedure is examined. The projection procedure gives a reduced-order pole assignment problem. It is shown that the sensitivities of the assigned poles in the complete closed-loop system are bounded in terms of the sensitivities of the assigned reduced-order poles, and the sensitivities of the unaltered poles are bounded in terms of the sensitivities of the corresponding open-loop poles. If the assigned poles are well-separated from the unaltered poles, these bounds are expected to be tight. The projection procedure is described in [3], and techniques for finding robust (or insensitive) solutions to the reduced-order problem are given in [1], [2].

Hypothesis management in situation assessment

A situation assessment uses reports from sensors to produce hypotheses about a situation at a level of aggregation that is of direct interest to a military commander. A low level of aggregation could mean forming tracks from reports, which is well documented in the tracking literature as track initiation and data association. In this paper there is also discussion on higher level aggregation; assessing the membership of tracks to larger groups. Ideas used in joint tracking and identification are extended, using multi-entity Bayesian networks to model a number of static variables, of which the identity of a target is one. For higher level aggregation a scheme for hypothesis management is required. It is shown how an offline clustering of vehicles can be reduced to an assignment problem.

HQET at order 1/m: II. Spectroscopy in the quenched approximation

Using Heavy Quark Effective Theory with non-perturbatively determined parameters in a quenched lattice calculation, we evaluate the splittings between the ground state and the first two radially excited states of the B(s) system at static order. We also determine the splitting between first excited and ground state, and between the B(s)* and B(s) ground states to order 1/m(b). The Generalized Eigenvalue Problem and the use of all-to-all propagators are important ingredients of our approach.

Busca heurística através de algoritmo genético e memético com construção de vocábulos para o problema de atribuição de localidades a anéis Sonet

Telecommunications play a key role in contemporary society. However, as new technologies are put into the market, it also grows the demanding for new products and services that depend on the offered infrastructure, making the problems of planning telecommunications networks, despite the advances in technology, increasingly larger and complex. However, many of these problems can be formulated as models of combinatorial optimization, and the use of heuristic algorithms can help solving these issues in the planning phase. In this project it was developed two pure metaheuristic implementations Genetic algorithm (GA) and Memetic Algorithm (MA) plus a third hybrid implementation Memetic Algorithm with Vocabulary Building (MA+VB) for a problem in telecommunications that is known in the literature as Problem SONET Ring Assignment Problem or SRAP. The SRAP arises during the planning stage of the physical network and it consists in the selection of connections between a number of locations (customers) in order to meet a series of restrictions on the lowest possible cost. This problem is NP-hard, so efficient exact algorithms (in polynomial complexity ) are not known and may, indeed, even exist

Heurísticas usando construção de vocabuilário aplicadas ao problema da atribuição de localidades a anéis em redes SONET/SDH

The SONET/SDH Ring Assignment Problem (PALAS) treats to group localities in form of some rings, being respected the traffic's limitations of the equipment. Each ring uses a DXC (Digital Cross Connect) to make the communication with the others, being the DXC the equipment most expensive of the net, minimizing the number total of rings, will minimize the total net cost, problem's objective . This topology in rings provides a bigger capacity of regeneration. The PALAS is a problem in Combinatorial Optimization of NP-hard Class. It can be solved through Heuristics and Metaheuristics. In this text, we use Taboo Search while we keep a set of elite solutions to be used in the formation of a part of the collection of vocabulary's parts that in turn will be used in the Vocabulary Building. The Vocabulary Building will be started case Taboo Search does not reach the best solution for the instance. Three approaches had been implemented: one that only uses vocabulary's parts deriving of Taboo Search, one that it only uses vocabulary's parts randomly generated and a last one that it uses half come of the elite and half randomly generated

Algoritmo evolutivo paralelo para o problema de atribuição de localidades a anéis em redes sonet/sdh

The telecommunications play a fundamental role in the contemporary society, having as one of its main roles to give people the possibility to connect them and integrate them into society in which they operate and, therewith, accelerate development through knowledge. But as new technologies are introduced on the market, increases the demand for new products and services that depend on the infrastructure offered, making the problems of planning of telecommunication networks become increasingly large and complex. Many of these problems, however, can be formulated as combinatorial optimization models, and the use of heuristic algorithms can help solve these issues in the planning phase. This paper proposes the development of a Parallel Evolutionary Algorithm to be applied to telecommunications problem known in the literature as SONET Ring Assignment Problem SRAP. This problem is the class NP-hard and arises during the physical planning of a telecommunication network and consists of determining the connections between locations (customers), satisfying a series of constrains of the lowest possible cost. Experimental results illustrate the effectiveness of the Evolutionary Algorithm parallel, over other methods, to obtain solutions that are either optimal or very close to it

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

Relax and cut: limitantes duais para o problema do caixeiro viajante

Pós-graduação em Matemática - IBILCE

Decomposition Methods and Network Design Problems

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).

Effiziente, operative Torbelegung mittels kombinatorischer Auktionen

Das operative Torbelegungsproblem (TBP) z. B. an einem Distributions- oder Cross-dockingzentrum ist ein logistisches Problem, bei dem es gilt, an- und abfahrende Fahrzeuge zeitlich und räumlich so auf die Warenein- und -ausgangstore zu verteilen, dass eine mög-lichst kostengünstige Abfertigung ermöglicht wird. Bisherige Arbeiten, die sich mit dem TBP beschäftigen, lassen Aspekte der Kooperation außer Acht. Dieser Beitrag stellt ein Verfahren vor, durch das der Nachteil einseitig optimaler Torbelegungen überwunden werden kann. Dabei wird auf das Mittel der kombinatorischen Auktionen zurückgegriffen und das TBP als Allokationsproblem modelliert, bei dem Frachtführer um Bündel konsekutiver Einheitszeit-intervalle an den Toren konkurrieren. Mittels eines Vickrey-Clarke-Groves-Mechanismus wird einerseits die Anreizkompatibilität, andererseits die individuelle Rationalität des Auk-tionsverfahrens sichergestellt. Das Verfahren wurde in ILOG OPL Studio 3.6.1 implemen-tiert und die durch Testdaten gewonnenen Ergebnisse zeigen, dass die Laufzeiten gering genug sind, um das Verfahren für die operative (kurzfristige) Planung einzusetzen und so transportlogistische Prozesse für alle Beteiligten wirtschaftlicher zu gestalten.

An MILP-based heuristic for staff scheduling problems with acceptance levels

We present a real-world staff-assignment problem that was reported to us by a provider of an online workforce scheduling software. The problem consists of assigning employees to work shifts subject to a large variety of requirements related to work laws, work shift compatibility, workload balancing, and personal preferences of employees. A target value is given for each requirement, and all possible deviations from these values are associated with acceptance levels. The objective is to minimize the total number of deviations in ascending order of the acceptance levels. We present an exact lexicographic goal programming MILP formulation and an MILP-based heuristic. The heuristic consists of two phases: in the first phase a feasible schedule is built and in the second phase parts of the schedule are iteratively re-optimized by applying an exact MILP model. A major advantage of such MILP-based approaches is the flexibility to account for additional constraints or modified planning objectives, which is important as the requirements may vary depending on the company or planning period. The applicability of the heuristic is demonstrated for a test set derived from real-world data. Our computational results indicate that the heuristic is able to devise optimal solutions to non-trivial problem instances, and outperforms the exact lexicographic goal programming formulation on medium- and large-sized problem instances.

Robustness and Recoverability in Transport Logistics

Abstract Transport is the foundation of any economy: it boosts economic growth, creates wealth, enhances trade, geographical accessibility and the mobility of people. Transport is also a key ingredient for a high quality of life, making places accessible and bringing people together. The future prosperity of our world will depend on the ability of all of its regions to remain fully and competitively integrated in the world economy. Efficient transport is vital in making this happen. Operations research can help in efficiently planning the design and operating transport systems. Planning and operational processes are fields that are rich in combinatorial optimization problems. These problems can be analyzed and solved through the application of mathematical models and optimization techniques, which may lead to an improvement in the performance of the transport system, as well as to a reduction in the time required for solving these problems. The latter aspect is important, because it increases the flexibility of the system: the system can adapt in a faster way to changes in the environment (i.e.: weather conditions, crew illness, failures, etc.). These disturbing changes (called disruptions) often enforce the schedule to be adapted. The direct consequences are delays and cancellations, implying many schedule adjustments and huge costs. Consequently, robust schedules and recovery plans must be developed in order to fight against disruptions. This dissertation makes contributions to two different fields: rail and air applications. Robust planning and recovery methods are presented. In the field of railway transport we develop several mathematical models which answer to RENFE’s (the major railway operator in Spain) needs: 1. We study the rolling stock assignment problem: here, we introduce some robust aspects in order to ameliorate some operations which are likely to fail. Once the rolling stock assignment is known, we propose a robust routing model which aims at identifying the train units’ sequences while minimizing the expected delays and human resources needed to perform the sequences. 2. It is widely accepted that the sequential solving approach produces solutions that are not global optima. Therefore, we develop an integrated and robust model to determine the train schedule and rolling stock assignment. We also propose an integrated model to study the rolling stock circulations. Circulations are determined by the rolling stock assignment and routing of the train units. 3. Although our aim is to develop robust plans, disruptions will be likely to occur and recovery methods will be needed. Therefore, we propose a recovery method which aims to recover the train schedule and rolling stock assignment in an integrated fashion all while considering the passenger demand. In the field of air transport we develop several mathematical models which answer to IBERIA’s (the major airline in Spain) needs: 1. We look at the airline-scheduling problem and develop an integrated approach that optimizes schedule design, fleet assignment and passenger use so as to reduce costs and create fewer incompatibilities between decisions. Robust itineraries are created to ameliorate misconnected passengers. 2. Air transport operators are continuously facing competition from other air operators and different modes of transport (e.g., High Speed Rail). Consequently, airline profitability is critically influenced by the airline’s ability to estimate passenger demands and construct profitable flight schedules. We consider multi-modal competition including airline and rail, and develop a new approach that estimates the demand associated with a given schedule; and generates airline schedules and fleet assignments using an integrated schedule design and fleet assignment optimization model that captures the impacts of schedule decisions on passenger demand.