This research involves the development of a comprehensive decision support system for energy planning through the increased use of renewable energy sources, while still considering the role of existing electricity generating facilities. This dissertation focuses on energy planning at the regional level, with the Greater Southern Appalachian Mountain region chosen for analysis due to the dependence on coal as the largest source of generation and the availability of wind and solar resources within the region. The first stage of this planning utilizes a geographic information system (GIS) for the discovery of renewable energy sources. This GIS model analyzes not just the availability of wind and solar power based on resource strength, but also considers the geographic, topographic, regulatory, and other constraints that limit the use of these resources. The model determines potential wind and solar sites within the region based on these input constraints, and finally the model calculates the cost and generation characteristics for each site. The results of the GIS model are then input into the second section of the model framework which utilizes a multi-objective linear programming (MOLP) model to determine the optimal mix of new renewable energy sources and existing fossil fuel facilities. In addition to the potential wind and solar resources discovered in the GIS, the MOLP model considers the implementation of solid wood waste biomass for co-fire at coal plants. The model consists of two competing objectives, the minimization of annual generation cost and the minimization of annual greenhouse gas emissions, subject to constraints on electricity demand and capital investment, amongst others. The model uses the MiniMax function in order to find solutions that consider both of the objective functions. The third major section of this dissertation analyzes three potential public policies — renewable portfolio standard, carbon tax, and renewable energy production tax credit — that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases are then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region. / Ph. D.

Sustainability is increasingly becoming a significant part of strategic asset management worldwide. Road agencies are providing guidelines to assess the relative sustainability of road projects. Unfortunately, environmental features of a road project are still considered as stand-alone evaluations, an added value. Very little has been done to integrate environmental impacts as a part of pavement management systems and other decision support tools to choose between different strategies. In this way, being awarded with a "green" certificate for a specific road project could result in the belief that recognition would correspond to the optimal strategy. Furthermore, a road project awarded with a "green" rating during the construction phase does not mean that the project results "green" if a life cycle approach is considered. Indeed, the most environmental friendly strategies may not be the ones with the highest performance. Using "greener" materials or performing recycle-related practices may lead to a lower performance over the life cycle and therefore produce an increase in maintenance needed, which could in turn result into more congestion due to work zones and higher total emissions. Therefore, construction and maintenance strategies should be analyzed according to three main parameters: cost, performance or effectiveness, and environmental impacts. The cost analysis part takes into account outflows over the service life of the pavement according to the well-known Life Cycle Cost Analysis methodology. The cheapest maintenance technique over the analysis period was expounded and sensitivity analyses to involved factors were conducted. Performance assessment was developed according to experimental on site data gathered and analyzed over several years to develop deterioration pavement models. Effectiveness of maintenance treatments is further provided and compared to the volume of traffic. In addition, environmental impacts related to maintenance and rehabilitation strategies were analyzed. Emissions were computed over the life cycle of the pavement from the manufacture of raw materials for the initial construction, placement, and maintenance phase. Finally, an optimization procedure was developed for including environmental impacts into a Pavement Management System. A methodology to set a multi-attribute approach system, computing costs, performance, and eco-efficiency over the life cycle of the pavement, is therefore proposed. / Ph. D.

Lower-cost manufacturing methods have increased the anticipation for economical mass production of vehicles manufactured from composite materials. One of the potential applications of composite materials in vehicles is in energy-absorbing components such as hollow shells and struts (these components may be in the form of circular cylindrical shells, square and rectangular tubes, conical shells, and frusta). However, constructions which result in brittle fracture of the composite tubes in the form of circumferential or longitudinal corner crack propagation may lead to unstable collapse failure mode and concomitant very low energy absorption. As a result, metal-composite hollow tubes have been developed that combine the benefits of stable ductile collapse of the metal (which can absorb crushing energy in a controlled manner) and the high strength-to-weight ratio of the composites. The relative and absolute thicknesses of metal or FRP section has a substantial effect on energy absorption of the hybrid tubes. In particular, likelihood of delamination occurrence raises with increase in FRP thickness. This can reduce the energy absorption capability of the metal-FRP hybrid tubes. Additionally, adding a very thick FRP section may result in a global buckling failure mode (rather than local folding). Until now, there are no studies specifically addressing the effect of FRP thickness on energy absorption of hybrid tubes. In this study, the effects of fiber orientation and FRP thickness (the number of layers) on the energy absorption of S2-glass/epoxy-304 stainless steel square tubes were experimentally investigated. In addition, a new geometrical trigger was demonstrated which has positive effects on the collapse modes, delamination in the FRP, and the crush load efficiency of the hybrid tube. To complete this study, a new methodology including the combination of experimental results, numerical modeling, and a multi-objective optimization process was introduced to obtain the best combination of design variables for hybrid metal-composite tubes for crashworthiness applications. The experimental results for the S2 glass/epoxy-304 stainless steel square tubes with different configurations tested under quasi-static compression loading were used to validate numerical models implemented in LS-DYNA software. The models were able to capture progressive failure mechanisms of the hybrid tubes. In addition, the effects of the design variables on the energy absorption and failure modes of the hybrid tubes were explained. Subsequently, the results from the numerical models were used to obtain optimum crashworthiness functions. The load efficiency factor (the ratio of mean crushing load to maximum load) and ratio between the difference of mean crushing load of hybrid and metal tube and thickness of the FRP section were introduced as objective functions. To connect the variables and the functions, back-propagation artificial neural networks (ANN) were used. The Non-dominated Sorting Genetic Algorithm–II (NSGAII) was applied to the constructed ANNs to obtain optimal results. The results were presented in the form of Pareto frontiers to help designers choose optimized configurations based on their manufacturing limitations. Such restrictions may include, but are not limited to, cost (related to the number of layers), laminate architecture (fiber orientation and stacking sequence) which can be constrained by the manufacturing techniques (i.e. filament winding) and thickness (as an example of physical constraints). / Ph. D.

With the proliferation of fairly powerful mobile devices and ubiquitous wireless technology, traditional mobile ad hoc networks (MANETs) now migrate into a new era of service-oriented MANETs wherein a node can provide and receive service from other nodes it encounters and interacts with. This dissertation research concerns trust management and its applications for service-oriented MANETs to answer the challenges of MANET environments, including no centralized authority, dynamically changing topology, limited bandwidth and battery power, limited observations, unreliable communication, and the presence of malicious nodes who act to break the system functionality as well as selfish nodes who act to maximize their own gain. We propose a context-aware trust management model called CATrust for service-oriented ad hoc networks. The novelty of our design lies in the use of logit regression to dynamically estimate trustworthiness of a service provider based on its service behavior patterns in a context environment, treating channel conditions, node status, service payoff, and social disposition as 'context' information. We develop a recommendation filtering mechanism to effectively screen out false recommendations even in extremely hostile environments in which the majority recommenders are malicious. We demonstrate desirable convergence, accuracy, and resiliency properties of CATrust. We also demonstrate that CATrust outperforms contemporary peer-to-peer and Internet of Things trust models in terms of service trust prediction accuracy against collusion recommendation attacks. We validate the design of trust-based service management based on CATrust with a node-to-service composition and binding MANET application and a node-to-task assignment MANET application with multi-objective optimization (MOO) requirements. For either application, we propose a trust-based algorithm to effectively filter out malicious nodes exhibiting various attack behaviors by penalizing them with trust loss, which ultimately leads to high user satisfaction. Our trust-based algorithm is efficient with polynomial runtime complexity while achieving a close-to-optimal solution. We demonstrate that our trust-based algorithm built on CATrust outperforms a non-trust-based counterpart using blacklisting techniques and trust-based counterparts built on contemporary peer-to-peer trust protocols. We also develop a dynamic table-lookup method to apply the best trust model parameter settings upon detection of rapid MANET environment changes to maximize MOO performance. / Ph. D.

Agricultural production is an important source of environmental emissions. While water quality concerns related to animal agriculture have been studied extensively, air quality issues have become an increasing concern. Due to the transfer of nutrients between air, water, and soil, emissions to air can harm water quality. We conduct a multi-objective optimization analysis for a representative dairy farm with two different approaches: nonlinear programming (NLP) and ϵ-constraint optimization to evaluate trade-offs among reduction of multiple pollutants including nitrogen (N), phosphorus (P), greenhouse gas (GHG), and ammonia. We evaluated twenty-six different scenar- ios in which we define incremental reductions of N, P, ammonia, and GHG from five to 25% relative to a baseline scenario. The farm entails crop production, livestock production (dairy and broiler), and manure management activities. Results from NLP optimization indicate that reducing P and ammonia emissions is relatively more expen- sive than N and GHG. This result is also confirmed by the ϵ-constraint optimization. However, the latter approach provides limited evidence of trade-offs among reduction of farm pollutants and net returns, while the former approach includes different re- duction scenarios that make trade-offs more evident. Results from both approaches indicate changes in crop rotation and land retirement are the best strategies to reduce N and P emissions while cow diet changes involving less forage represents the best strategy to reduce ammonia and GHG emissions. / Master of Science / Human activities often damage and deplete the environment. For instance, nutrient pollution into air and water, which mostly comes from agricultural and industrial activ- ities, results in water quality degradation. Thus, mitigating the detrimental impacts of human activities is an important step toward environmental sustainability. Reducing environmental impacts of nutrient pollution from agriculture is a complicated problem, which needs a comprehensive understanding of types of pollution and their reduction strategies. Reduction strategies need to be both feasible and financially viable. Con- sequently, practices must be carefully selected to allow farmers to maximize their net return while reducing pollution levels to reach a satisfactory level. Thus, this paper conducts a study to evaluate the trade-offs associated with farm net return and re- ducing the most important pollutants generated by agricultural activities. The results of this study show that reducing N and GHG emissions from a representative dairy farm is less costly than reducing P and ammonia emissions, respectively. In addition, reducing one pollutant may result in reduction of other pollutants. In general, for N and P emissions reduction land retirement and varying crop rotations are the most effective strategies. However, for reducing ammonia and GHG emissions focusing on cow diet changes involving less forage is the most effective strategy.

Dependence on fossil fuels in the electric sector is one of the major contributors towards Greenhouse gas (GHG) emissions. The increase in renewable contribution has been observed in recent years but there is still potential to utilize wood waste in rural communities for electricity generation promoting energy independence and sustainable development. For this study, a life cycle assessment approach was utilized to estimate the emissions of electricity produced from wood residue in a rural community. Therefore, the process from planting to supply for bioenergy facility to generate electricity are included. The results showed a decrease of 92-96 % in global warming potential resulting from the use of wood residues as compared to that of Grid electricity, natural gas, and coal-fired power plants. Then, a two-layer supply chain network comprising of feedstock supply sites and candidate power plant locations are considered to determine ideal locations for facilitating the bioenergy facility to minimize overall system cost and GHG emissions. The multi objective mathematical model aims to handle various decisions such as power plant location and technology selection, allocation of suppliers to power plants, biomass harvesting, storage, and transportation decisions in the considered supply chain network. The model developed was applied to case study region of Grenada County, Mississippi. The solution with no GHG restriction facilitates higher power plant capacity, 25 MW with lower system cost and satisfies 32.11 % of the total electricity demand of the case study area. Whereas the solution with highest GHG restrictions reduces the power plant capacity to 10 MW, that satisfies 10.22 % of the total electricity demand with increase in total overall system due to the increase in purchase of electricity from external sources as penalty cost. Furthermore, the investigation was extended to multiple counties of Mississippi to determine the feasibility of bioenergy facilities to be located using wood waste as fuel source. The techno-enviro-economic assessment showed the competitiveness of LCOE with the existing electricity supplier as well as other renewable sources such as solar, and wind. The findings of this research can facilitate in decision making process for promoting renewable energy in existing energy supply sources.

The automatic synthesis of embodied creatures through artificial evolution has become a key area of research in robotics, artificial life and the cognitive sciences. However, the research has mainly focused on genetic encodings and fitness functions. Considerably less has been said about the role of controllers and how they affect the evolution of morphologies and behaviors in artificial creatures. Furthermore, the evolutionary algorithms used to evolve the controllers and morphologies are pre-dominantly based on a single objective or a weighted combination of multiple objectives, and a large majority of the behaviors evolved are for wheeled or abstract artifacts. In this thesis, we present a systematic study of evolving artificial neural network (ANN) controllers for the legged locomotion of embodied organisms. A virtual but physically accurate world is used to simulate the evolution of locomotion behavior in a quadruped creature. An algorithm using a self-adaptive Pareto multi-objective evolutionary optimization approach is developed. The experiments are designed to address five research aims investigating: (1) the search space characteristics associated with four classes of ANNs with different connectivity types, (2) the effect of selection pressure from a self-adaptive Pareto approach on the nature of the locomotion behavior and capacity (VC-dimension) of the ANN controller generated, (3) the effciency of the proposed approach against more conventional methods of evolutionary optimization in terms of computational cost and quality of solutions, (4) a multi-objective approach towards the comparison of evolved creature complexities, (5) the impact of relaxing certain morphological constraints on evolving locomotion controllers. The results showed that: (1) the search space is highly heterogeneous with both rugged and smooth landscape regions, (2) pure reactive controllers not requiring any hidden layer transformations were able to produce sufficiently good legged locomotion, (3) the proposed approach yielded competitive locomotion controllers while requiring significantly less computational cost, (4) multi-objectivity provided a practical and mathematically-founded methodology for comparing the complexities of evolved creatures, (5) co-evolution of morphology and mind produced significantly different creature designs that were able to generate similarly good locomotion behaviors. These findings attest that a Pareto multi-objective paradigm can spawn highly beneficial robotics and virtual reality applications.

Les réseaux de capteurs ont connu un grand essor ces dix dernières années. Ils interviennent dans tous les domaines de notre vie quotidienne et la rendent plus aisée. Malgré ce grand succès des réseaux de capteurs, plusieurs problèmes restent encore ouverts. La capacité énergétique et la fragilité du canal radio des réseaux de capteurs affectent gravement leurs performances. La communication coopérative représente une solution efficace pour lutter contre l'instabilité du canal radio et afin d'économiser plus d'énergie. Nous proposons dans ce manuscrit, d'utiliser la communication coopérative, en premier lieu, au niveau de la couche MAC afin de mettre en place un accès au canal coopératif et non égoïste. En second lieu, nous utilisons la communication coopérative au niveau de la couche réseau dans le but d'établir des chemins de routage plus stables et plus robustes. / Wireless sensor networks (WSN) have known a great development during the last decade. They intervene in all the domain of our everyday life to make it easier. Despite the success of WSN several problems have to be solved. The restricted energy capacity and the randomness of the wireless channel seriously affect the performances of the WSN. Cooperative communication represents an efficient solution to reduce the instability of the wireless channel and to optimize energy. In this thesis we propose to use cooperative communications at the MAC and network layer in order to set up a cooperative access to the channel and to establish more robust routing paths.

This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this type of problem, there is no unique solution. However, out of many available options, the Decision Maker eventually has to choose only one solution. With this problem as the motivation, the current study develops a compromise ranking algorithm, namely a trade-off ranking method. The trade-off ranking method able to give a trade-off solution with the least compromise compared to other choices as the best solution. The properties of the algorithm are studied in the thesis on several test cases. The proposed method is compared against several multi-criteria decision making methods with ranking based on the distance measure, which are the TOPSIS, relative distance and VIKOR. The sensitivity analysis and uncertainty test are carried out to examine the methods robustness. A critical criteria analysis is also done to test for the most critical criterion in a multi-criteria problem. The decision making method is considered further in a fuzzy environment problem where the fuzzy trade-off ranking is developed and compared against existing fuzzy decision making methods.

O problema de Planejamento da Expansão de Sistemas de Distribuição (PESD) visa determinar diretrizes para a expansão da rede considerando a crescente demanda dos consumidores. Nesse contexto, as empresas distribuidoras de energia elétrica têm o papel de propor ações no sistema de distribuição com o intuito de adequar o fornecimento da energia aos padrões exigidos pelos órgãos reguladores. Tradicionalmente considera-se apenas a minimização do custo global de investimento de planos de expansão, negligenciando-se questões de confiabilidade e robustez do sistema. Como consequência, os planos de expansão obtidos levam o sistema de distribuição a configurações que são vulneráveis a elevados cortes de carga na ocorrência de contingências na rede. Este trabalho busca a elaboração de uma metodologia para inserir questões de confiabilidade e risco ao problema PESD tradicional, com o intuito de escolher planos de expansão que maximizem a robustez da rede e, consequentemente, atenuar os danos causados pelas contingências no sistema. Formulou-se um modelo multiobjetivo do problema PESD em que se minimizam dois objetivos: o custo global (que incorpora custo de investimento, custo de manutenção, custo de operação e custo de produção de energia) e o risco de implantação de planos de expansão. Para ambos os objetivos, são formulados modelos lineares inteiros mistos que são resolvidos utilizando o solver CPLEX através do software GAMS. Para administrar a busca por soluções ótimas, optou-se por programar em linguagem C++ dois Algoritmos Evolutivos: Non-dominated Sorting Genetic Algorithm-2 (NSGA2) e Strength Pareto Evolutionary Algorithm-2 (SPEA2). Esses algoritmos mostraram-se eficazes nessa busca, o que foi constatado através de simulações do planejamento da expansão de dois sistemas testes adaptados da literatura. O conjunto de soluções encontradas nas simulações contém planos de expansão com diferentes níveis de custo global e de risco de implantação, destacando a diversidade das soluções propostas. Algumas dessas topologias são ilustradas para se evidenciar suas diferenças. / The Distribution System Expansion Planning (DSEP) problem aims to determine guidelines to expand the network considering the growing demand of customers. In this context, the distribution companies have to propose actions for improvements in the distribution system in order to adjust the supply of energy to the standards required by regulators. Traditionally minimizing the global cost of expansion plans is the only goal that is considered, thus reliability and robustness issues are neglected. As a result, the optimal expansion plans lead the distribution system to configurations that are vulnerable to high load shedding under the occurrence of contingencies in the network. This work aims to develop a methodology to insert reliability and risk issues to the traditional DSEP problem in order to maximize the robustness of the network and hence mitigate the system damages caused by contingencies. We formulated a multi-objective model of the problem that compromises two objectives: minimization of the global cost (that comprises investment cost, maintenance cost, operational cost, and production cost) and minimization of the deployment risk of expansion plans. For both objectives, we formulated mixed integer linear models which are solved using CPLEX accessed through GAMS. To manage the search for optimal solutions, we chose to implement in C++ language two Evolutionary Algorithms (EAs): Non-dominated Sorting Genetic Algorithm-2 (NSGA2) and Strength Pareto Evolutionary Algorithm-2 (SPEA2). The effectiveness of both algorithms was verified through simulations of the expansion planning of two test systems, adapted from the literature. The set of solutions that has been found contains expansion plans with different levels of global cost and deployment risk. Some of these topologies are depicted to show this diversity of the proposed solutions.