Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

Network virtualization has become a primary enabler to solve the internet ossi- fication problem. It allows to run multiple architectures or protocols on a shared physical infrastructure. One of the important aspects of network virtualization is to have a virtual network (VN) mapping technique which uses the substrate resources efficiently. Currently, there exists very few VN mapping techniques and there is no common evaluation strategy which can test these algorithms effectively. In this thesis, we advocate the need for such a tool and develop it by considering a wide spectrum of parameters and simulation scenarios. We also provide various performance metrics and do a comparison study of the existing algorithms. Based on the comparative study, we point out the positives and negatives of the existing mapping algorithms and propose a new LP formulation based on Hub location approach that efficiently allocates substrate resources to the virtual network requests. Our results show that our algorithm does better in terms of number of successful network mappings and average time to map while balancing load on the network.

Le succès dans l'utilisation de méthodes exactes d’optimisation combinatoire pour des problèmes de grande taille est encore limité à certains problèmes ou à des classes spécifiques d'instances de problèmes. Une approche alternative consiste soit à utiliser des métaheuristiques ou des matheuristiques qui reposent en partie sur des méthodes exactes. Dans le contexte de l'optimisation combinatoire, nous nous intéressons des heuristiques permettant de choisir les heuristiques appliquées au problème traité. Dans cette thèse, nous nous concentrons sur l'optimisation à l’aide d’hyperheuristiques pour des problèmes logistiques. Nous proposons un cadre hyperheuristique qui effectue une recherche dans l'espace des algorithmes heuristiques et apprend comment changer l'heuristique courante systématiquement tout au long du processus de telle sorte qu'une bonne séquence d'heuristiques permet d’obtenir des solutions de haute qualité. Nous étudions plus particulièrement deux problèmes en logistique pour lesquels nous proposons des HHs: un problème de planification d’interventions sur des puits de forage et un problème conjoint de localisation de hubs et de routage. Ensuite, nous comparons les performances de plusieurs HH décrites dans la littérature pour le second problème abordé reposant sur différentes méthodes de sélection heuristique telles que la sélection aléatoire, la fonction de choix, une approche de Q-Learning et un algorithme de colonie de fourmis. Les résultats numériques prouvent l'efficacité de HHs pour les deux problèmes traités, et la pertinence d'inclure l'information venant d’une relaxation de Lagrangienne pour le deuxième problème. / Success in using exact methods for large scale combinatorial optimization is still limited to certain problems or to specific classes of instances of problems. The alternative way is either using metaheuristics or matheuristics that rely on exact methods in some ways. In the context of combinatorial optimization, we are interested in heuristics to choose heuristics invoked to solve the addressed problem. In this thesis, we focus on hyperheuristic optimization in logistic problems. We focus on proposing a hyperheuristic framework that carries out a search in the space of heuristic algorithms and learns how to change the incumbent heuristic in a systematic way along the process in such a way that a good sequence of heuristics produces high quality solutions. We propose HHs for two problems in logistics: the workover rig scheduling problem and the hub location routing problem. Then, we compare the performances of several HHs described in the literature for the latter problem, which embed different heuristic selection methods such as a random selection, a choice function, a Q-Learning approach, and an ant colony based algorithm. The computational results prove the efficiency of HHs for the two problems in hand, and the relevance of including Lagrangian relaxation information for the second problem.

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[pt] A relação entre as atividades logísticas desempenhadas nas empresas de telecomunicações e sua prestação de serviço parece, para o público em geral, estarem desassociadas. Entretanto, a necessidade de atendimento de áreas extensas associadas a redução custos, coloca essas atividades, ditas não-essenciais, no grupo de atividades estratégicas. Através da introdução do ambiente de telecomunicações brasileiro, da importância da logística para este serviço e do estudo de problemas de localização, a presente dissertação de mestrado desenvolve um modelo MIP - Mix Integer Programming – dinâmico para o problema de localização de hubs conhecido como: ULP - Uncapacitated Hub Location Problem, sendo este modelo utilizado na análise de um estudo de caso real de uma operadora de serviços de telecomunicações via satélite, onde foram obtidos insights quanto o nível de redução de custo através do redesenho da rede de distribuição e da escolha de novos pontos de armazenagem, sendo comprovados através um estudo estocástico com 500 cenários aleatórios. / [en] The relationship between logistics activities performed on telecommunications companies and their service delivery seems, to the public, is disassociated. However, the need to service large areas associated with reducing costs, puts these activities nonessential into to the group of strategic activities. Through the introduction of the Brazilian telecommunications environment, the importance of logistics for this service and the study location problems, this master thesis develops a dynamic MIP model - Mix Integer Programming - for the hub location problem known as ULP - Uncapacitated Hub Location Problem, and this model is used in the analysis of a real case study of an satellite telecommunications operator. which were obtained insights into the level of reducing cost by redesigning of distribution network and the choice of new warehouse points, being demonstrated by a stochastic study of 500 random scenarios.