Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.

Remote sensors and unmanned aerial vehicles (UAVs) have the potential to dramatically improve infrastructure health monitoring in terms of accuracy of the information and frequency of data collection. UAV automation has made significant progress but that automation is also creating vast amounts of data that needs to be processed into actionable information. A key aspect of this work is the optimization (not just automation) of data collection from UAVs for targeted planning of mission objectives. This work investigates the use of camera planning for Structure from Motion for 3D modeling of infrastructure. Included in this thesis is a novel multi-scale view-planning algorithm for autonomous targeted inspection. The method presented reduced the number of photos needed and therefore reduced the processing time while maintaining desired accuracies across the test site. A second focus in this work investigates various set covering problem algorithms to use for selecting the optimal camera set. The trade-offs between solve time and quality of results are explored. The Carousel Greedy algorithm is found to be the best method for solving the problem due to its relatively fast solve speeds and the high quality of the solutions found. Finally, physical flight tests are used to demonstrate the quality of the method for determining coverage. Each of the set covering problem algorithms are used to create a camera set that achieves 95% coverage. The models from the different camera sets are comparable despite having a large amount of variability in the camera sets chosen. While this study focuses on multi-scale view planning for optical sensors, the methods could be extended to other remote sensors, such as aerial LiDAR.

No description available.

Municipalities in the Czech Republic deal with their budget, which among others consists of granted subsidies from the region, State, European Union or other organizations. Nowadays the budget transactions are not being under the control. In the future, it is appropriate to introduce some external view to control their spending. Establishment of an audit service for each municipality would be financially inevitable. Therefore it is suggested to provide the State audit services only in some municipalities and to share them with more municipalities within one region. Deployment of the audit centers and assigning municipalities lead to solving a linear problem which falls under the covering problem class. The establishment of audit centers is only illustrative, the employment of more shared state services could follow a similar principle.

No description available.

The present thesis shows the manner of distributing workers on shifts with regular work load but uneven work schedules. It is generally known as the covering or set-covering problem. The present paper describes a way to solve it with mathematical modelling and programs for possible planning of workers on shifts. The issue is documented on a case of an existing company.

This thesis applies classical optimization problems such as assignment or set-covering problem on logical puzzles or brainteasers. Listed in the first part are mathematical model, description and typical example of each optimization problem used in this thesis. The second part contains these models applied to the particular brainteasers for example Sudoku or Einstein's Puzzle. Exercises are divided into simpler and more complex ones. There is specification, source and a described method of solution stated for each of them. The calculation examples use Lingo or MS Excel or both. The aim is to show the possibility to address logical puzzles and brainteasers with the use of optimization problems, and thus confirm the wide possibilities of using these models. These examples can clarify and diversify the curriculum.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-12T13:49:50Z No. of bitstreams: 1 Deborah Luisa Detânico Vieira_.pdf: 1504339 bytes, checksum: 49a2adc770aff79d216c818e22dea099 (MD5) / Made available in DSpace on 2018-04-12T13:49:50Z (GMT). No. of bitstreams: 1 Deborah Luisa Detânico Vieira_.pdf: 1504339 bytes, checksum: 49a2adc770aff79d216c818e22dea099 (MD5) Previous issue date: 2017-05-17 / Nenhuma / Com a crescente demanda de serviços de voz e, principalmente, dados móveis se fez necessário o desenvolvimento das tecnologias de quarta geração (4G). O padrão Long Term Evolution (LTE), desenvolvido pela Third Generation Partnership Project (3GPP), foi escolhido pela International Telecommunications Union (ITU) como tecnologia para atender os requisitos da quarta geração de serviços móveis. Para as operadoras inserirem esta nova tecnologia em suas redes existentes, se faz necessário um estudo meticuloso de planejamento, muito embora, na prática, este planejamento seja desenvolvido de forma empírica. O problema de planejamento de redes é conhecido e bem estudado no ramo da computação, conhecido como problema de recobrimento de conjuntos e classificado, pela sua complexidade, como NP-difícil. Dadas as características diferenciadas da arquitetura da rede do LTE, este trabalho busca resolver o problema de planejamento de redes de quarta geração (4G), utilizando uma modelagem matemática aplicada a uma metaheurística híbrida, composta de Algoritmo Genético e Busca Tabu. Almejase resolver o problema de cobertura de uma determinada região, cobrindo a maior área possível com o menor número possível de Base Stations (BS), visando ao planejamento com maior assertividade e redução do custo de implantação da rede 4G. / With the constantly demand of voice services and mostly in mobile data, there was the need the development of the mobile services of fourth generation (4G). The pattern Long Term Evolution, developed by the Third Generation Partnership Project (3GPP) was chosen by the International Telecommunications Union (ITU) as technology to attend the requirements of the fourth generation of mobile services. For the mobile operators introduce and apply this new generation in their own existing networks, they need to do an extensive research and planning, even if, in practical means, it is applied using the empirical way. The network planning problem is widely known and studied in computing area as set-covering problem ans classified as NPhard. Due the unique characteristics of network architecture of LTE, this work aims to solve the mobile’s fourth generation planning problem using a mathematics modelling apply to a hybrid metaheuristics, composed with Genetic Algorithm and Tabu Search. It aims solve the coverage problem of a specific region, covering the largest area possible with the fewest number of Base Sations (BS) possible, seeking the best compliance and cost reduction of the LTE network deployment.

Στην παρούσα μεταπτυχιακή εργασία περιγράφεται η διαδικασία συνδυασμού προβλημάτων Επιχειρησιακής Έρευνας με την μεθοδολογία εύρεσης συγκριτικής αποδοτικότητας (DEA). Αρχικά, παρουσιάζεται μια γενική περιγραφή της μεθόδου DEA και μια συνοπτική επισκόπηση της σχετικής βιβλιογραφίας. Παρουσιάζεται ο τρόπος συνδυασμού της μεθόδου DEA και δύο κλασσικών μοντέλων χωροθέτησης εγκαταστάσεων, του μοντέλου με περιορισμό και του αντίστοιχου μοντέλου χωρίς περιορισμό στην χωρητικότητα. Για την επίτευξη αυτού του στόχου γίνονται οι απαραίτητοι χειρισμοί στην μέθοδο DEA ούτως ώστε να μπορεί να υπολογίζεται η αποδοτικότητα για όλες τις μονάδες λήψης απόφασης ταυτόχρονα – μέθοδος ταυτόχρονης DEA (Simultaneous DEA), εφόσον το κλασσικό μοντέλο βρίσκει την αποδοτικότητα μιας μονάδας λύνοντας μια φορά το γραμμικό πρόβλημα με τους συντελεστές βαρύτητας αυτής της μονάδας. Η λύση του πολυκριτήριου προβλήματος αναδεικνύει την αλληλεπίδραση μεταξύ κόστους και αποδοτικότητας, για τη λήψη απόφασης ανάλογα με τις ανάγκες που μπορεί ενυπάρχουν σε ένα αντίστοιχο πραγματικό πρόβλημα. Στην συνέχεια αναπτύσσεται για πρώτη φορά στη διεθνή βιβλιογραφία μια μεθοδολογία για το συνδυασμό δύο άλλων βασικών προβλημάτων, της κάλυψης και της σύμπτυξης συνόλου, αντίστοιχα, με την μεθοδολογία DEA. Στόχος είναι να μορφοποιηθεί ένα μοντέλο γραμμικού προγραμματισμού έτσι ώστε εκτός από το μέτρο απόφασης του κόστους για την κάλυψη ή σύμπτυξη ενός συνόλου-στόχου, από διαθέσιμα υποσύνολα να ληφθεί υπόψη και η αποδοτικότητα του εκάστοτε υποσυνόλου, η οποία εν τέλει θα επηρεάσει και την συνολική αποδοτικότητα του συνόλου-στόχου. Γίνεται ο συνδυασμός των μεθοδολογιών και αναπτύσσονται μεθοδολογίες πολυκριτήριας ανάλυσης που μπορούν να χρησιμοποιηθούν για την λήψη αποφάσεων που αφορούν την αποδοτική και οικονομική κάλυψη ή σύμπτυξη ενός συνόλου. Για την πιστοποίηση και τη διαπίστωση της λειτουργικότητας των προτεινόμενων μεθοδολογιών αναπτύσσονται παραδείγματα προβλημάτων, τα οποία και επιλύονται επιτυχώς. / In the present thesis, the combination of Operation Research Problems with the Data Envelopment Analysis (DEA) is performed in order to make optimal and efficient decisions. Firstly, a general description of DEA and a breath literature review is presented. Then, we show and test location modeling formulations that utilize data envelopment analysis (DEA) efficiency measures to find optimal and efficient facility location/allocation patterns. In addition, to the authors’ best knowledge, the combinations of DEA with the Set Covering Problem as well as Set Packing Problem are formulated as multiobjective problems, for first time in the literature. The main aim of the proposed models is to make cost-effective and efficient decisions regarding the Set Covering and Packing Problem, respectively. Numerical examples are developed in order to validate and test the novel models. The numerical results of multiobjective analysis demonstrate that the proposed methods are able to successfully find optimal and efficient solutions for real set covering, packing and partitioning problems.

We meet with solving of optimization problems every day, when we try to do our tasks in the best way. An Ant Colony Optimization is an algorithm inspired by behavior of ants seeking a source of food. The Ant Colony Optimization is successfuly using on optimization tasks, on which is not possible to use a classical optimization methods. A Genetic Algorithm is inspired by transmision of a genetic information during crossover. The Genetic Algorithm is used for solving optimization tasks like the ACO algorithm. The result of my master's thesis is created simulator for solving choosen optimization tasks by the ACO algorithm and the Genetic Algorithm and a comparison of gained results on implemented tasks.