Τα απόβλητα, που παράγονται από τα ελαιοτριβεία, τυροκομεία και βουστάσια είναι τα παραπροϊόντα της λειτουργίας τέτοιων μονάδων. Στην Ελλάδα οι δραστηριότητες αυτές έχουν σημαντικό οικονομικό και κοινωνικό αντίκτυπο, καθώς μεγάλο μέρος του πληθυσμού συμμετέχει ενεργά σε όλα τα στάδια της παραγωγής έχοντας σημαντικό οικονομικό όφελος. Ιδιαίτερα στην περίπτωση του ελαιολάδου η Ελλάδα αποτελεί την τρίτη μεγαλύτερη χώρα παραγωγής παγκοσμίως μετά την Ισπανία και την Ιταλία. Το κύριο πρόβλημα, που προκύπτει είναι η παραγωγή τεράστιων ποσοτήτων αποβλήτων, που με τα υψηλά οργανικά φορτία τους καθίστανται εξαιρετικά επικίνδυνα και η διαχείρισή τους πολύ δύσκολη. Ο σκοπός της παρούσας εργασίας είναι η τεχνική και οικονομική ανάλυση μιας μονάδας επεξεργασίας αυτών των αποβλήτων, όπου αυτά θα χρησιμοποιούνται ως πρώτη ύλη για την παραγωγή ενέργειας. Τα οφέλη μια τέτοιας μονάδας θα είναι πολύ μεγάλα καθώς τα απόβλητα πλέον δεν θα εναποτίθενται ανεξέλεγκτα στο περιβάλλον, μειώνοντας έτσι τη ρύπανση, ενώ μέσω της εξασφαλισμένης πώλησης της ηλεκτρικής και θερμικής ενέργειας και στερεού καυσίμου, που θα παράγονται στη μονάδα, η επένδυση θα καταστεί οικονομικά βιώσιμη. Η μονάδα θα λειτουργεί με βάση της διαδικασία της Αναερόβιας Συγχώνευσης οργανικών υποστρωμάτων, η οποία αποτελεί μία από τις βέλτιστες προτεινόμενες τεχνολογίες για την αποδόμηση υπολειμμάτων υψηλού οργανικού φορτίου με ελάχιστο τεχνολογικό ρίσκο. Επίσης, μέρος της εργασίας αποτελεί και η βελτιστοποίηση της δρομολόγησης των βυτιοφόρων οχημάτων, τα οποία θα συλλέγουν τα απόβλητα από τις μονάδες παραγωγής τους και θα τα συγκεντρώνουν στη μονάδα επεξεργασίας. Η βελτιστοποίηση αυτής της διαδικασίας έγινε μέσω ανάπτυξης κατάλληλου αλγορίθμου και επίλυσης αυτού με το υπολογιστικό πρόγραμμα General Algebraic Modeling System (GAMS), για τον υπολογισμό των βέλτιστων διαδρομών, των μεταφερόμενων φορτίων καθώς και του απαιτούμενου αριθμού βυτιοφόρων οχημάτων με βάση πάντα την ελαχιστοποίηση του κόστους μεταφοράς και συλλογής. / The produced wastes by olive mills, dairies and cow farms are by-products from the operation of such units. In Greece these activities have significant economic and social impact since a large part of the population actively participates in all stages of production having serious financial gain. Especially in the case of olive oil, Greece is the third largest producer worldwide after Spain and Italy. The main problem that arises is the production of huge quantities of wastes, which due to their high organic loads are extremely dangerous for the environment and public health while their management is quite difficult. The aim of this work is the technical and economic analysis of a plant processing these agroindustrial wastes, where they will be used as feedstock for energy and solid fuel (pellet) production. The benefits from the operation of such a plant are substantial since wastes will no longer be uncontrollably disposed in the environment, reducing pollution, while through the secured sale of the produced thermal and electric energy and solid fuel the overall investment will be economically viable and attractive to potential investors. Such a plant will be operated using the anaerobic co-digestion process, which is a biological process (included in European best practices) for the degradation of organic residues under conditions of oxygen absence, with minimum technological and economic risk. Furthermore, part of this work was devoted to the optimization of the vehicles routing, which will collect the wastes and will transfer them at the processing plant. The optimization of this process was carried out via developing an appropriate algorithm which was solved using the computer program General Algebraic Modeling System (GAMS). The algorithm calculates the best routes, the amount of wastes transferred and the required number of vehicles based always on minimizing the cost of transportation and collection.

Many-objective optimization problems (MaOPs), in which the number of objectives is greater than three, are common in various applications, and have drawn many scholars' attention. Evolutionary multiobjective optimization (EMO) algorithms have been successfully applied to solve bi- and tri-objective optimization problems. However, MaOPs are more challenging compared with the bi- and tri-objective optimization problems. The performances of most existing classical EMO algorithms generally deteriorate over the number of objectives. Thus, this thesis presents a weight design method to modify classical decomposition-based EMO algorithms for solving MaOPs, and a novel objective extraction method to transform the MaOP into a problem with few objectives.;Additionally, performance metrics play an important role in understanding the strengths and weaknesses of an algorithm. To the best of our knowledge, there is no direct performance metric for the objective reduction algorithms. Their performance can only be indirectly evaluated by the metrics, such as IGD-metric and H-metric, of the solutions obtained by an EMO algorithm equipped with the objective reduction method. This thesis presents a direct performance metric featuring the simplicity and usability of the objective reduction algorithms. Meanwhile, we propose a novel framework for many-objective test problems, which features both simple and complicated Pareto set shape, and is scalable in terms of the numbers of the objectives and the essential objectives. Also, we can control the importance of essential objectives.;As some MaOPs may have redundant or correlated objectives, it is desirable to reduce the number of the objectives in such circumstances. However, the Pareto solution of the reduced problem obtained by most existing objective reduction methods may not be the Pareto solution of the original MaOP. Thus, this thesis proposes an objective extraction method for MaOPs. It formulates the reduced objective as a linear combination of the original objectives to maximize the conflict between the reduced objectives. Subsequently, the Pareto solution of the reduced problem obtained by the proposed algorithm is that of the original MaOP, and the proposed algorithm can preserve the non-dominant relation as much as possible. We compare the proposed objective extraction method with three objective reduction methods, i.e., REDGA, L-PCA and NL-MVU-PCA. The numerical studies show the effectiveness and robustness of the proposed approach.;The decomposition-based EMO algorithms, e.g. MOEA/D, M2M, have demonstrated the effectiveness in dealing with MaOPs. Nevertheless, these algorithms need to design the weight vectors, which has significant effects on the algorithms' performance. Especially, when the Pareto front of the problem is incomplete, these algorithms cannot obtain a set of uniform solutions by using the conventional weight design methods. Not only can self-organizing map (SOM) preserve the topological properties of the input data by using the neighborhood function, but also its display is more uniform than the probability density of the input data. This phenomenon is advantageous to generate a set of uniform weight vectors based on the distribution of the individuals. Therefore, we propose a novel weight design method based on SOM, which can be integrated with most of the decomposition-based EMO algorithms. In this thesis, we choose the existing M2M algorithm as an example for such integration. This integrated algorithm is then compared with the original M2M and two state-of-the-art algorithms, i.e. MOEA/D and NSGA-II on eleven redundancy problems and eight non-redundancy problems. The experimental results show the effectiveness of the proposed approach.

Facility Location problem refers to placing facilities (mostly vehicles) in appropriate locations to yield the best coverage with respect to other important factors which are specific to the problem. For instance in a fire station some of the important factors are traffic time, distribution of stations, time of the service and so on. Furthermore, budget limitation, time constraints and the great importance of the operation, make the optimum allocation very complex. In the past few years, several research in this area have been done to help managers by designing some effective algorithm to allocating facilities in the best way possible. Most early proposed models were focused on static and deterministic methods. In static models, once a facility assigns to a location, it will not relocate anymore. Although these methods could be utilized in some simple settings, there are so many factors in real world that make a static model of limited application. The demands may change over time or facilities may be dropped or added. In these cases a more flexible model is desirable, thus dynamic models are proposed to be used in such cases. Facilities can be located and relocated based on the situations. More recently, dynamic models became more popular but there were still many aspects of facility allocation problems which were challenging and would require more complex solutions. The importance of facility location problem becomes significantly more relevant when it relates to hospitals and emergency responders. Even one second of improvement in response time is important in this area. For this reason, we selected ambulance facility allocation problem as a case study to analyze this problem domain. Much research has been done on ambulances allocation. We will review some of these models and their advantages and disadvantages. One of the best model in this areas introduced by Rajagopalan. In this work, his model is analyzed and its major drawback is addressed by applying some modifications to its methodology. Genetic Algorithm is utilized in this study as a heuristic method to solve the allocation model.

This dissertation addresses the issue of optimum system design to achieve reliable communication in the presence of various types of interference. Multiobjective formulation is used with noncooperative and cooperative approaches owing to the nature of the problems under consideration. Since intentional Jamming is one of the most severe kinds of interference, anti-jam techniques are crucial for communications in a hostile environment. The jam and anti-jam problem is modeled as a two-person zero-sum game in which the communicator and the jammer have antagonistic objectives and are viewed as the two players. The concept of Nash equilibrium is introduced and its characterizations such as existence, uniqueness, stability, robustness, and sensitivity are investigated. This model is then applied to a frequency-hop spread spectrum M-ary frequency-shift-keying system where ratio-threshold diversity is used to combat partial-band noise and multitone jamming. Equilibrium performance in terms of cutoff rate and bit error rate is shown to be superior to that predicted by worst-case analysis. When mutual interference caused by simultaneous transmissions is the major concern in a heterogeneous packet network, a multiobjective framework is proposed in this dissertation with the objectives and constraints of the individual users taken into consideration. Near-far effect and Rayleigh fading may occasion packet capture and therefore create unfairness in favor of closer users. Thus, multiobjective optimality is introduced, in which criterion of fairness is embedded. Optimum strategies controlling transmission probability and/or power are examined to yield the Pareto optimal solution in a slotted ALOHA network. Then, the same control strategies are studied with the channel utilization being the maximization objective. Optimization results are obtained in various situations, and effectiveness of different strategies is compared. A multimedia direct-sequence spread spectrum system may support multiple services with different transmission rates and diverse quality-of-service requirements. To facilitate multimedia applications and maximize the system capacity, average power control, error correction coding, and time diversity are incorporated into the system. The capacity of such a system is evaluated in multipath Rayleigh fading channels. Average bit error rate, outage probability, and corresponding information theoretic bounds are discussed. Concatenation of Reed-Solomon codes and convolutional codes is considered for error correction to account for different quality and delay constraints. It is shown through a numerical example that the system capacity can be increased significantly by an appropriate system design. / Graduate

Master of Agribusiness / Department of Agricultural Economics / Brian C. Briggeman / Garden City Co-op, Inc. is a farm cooperative in Southwest Kansas. It provides marketing and storage of grain, fertilizer, crop protection products, seed, and petroleum to both member and non-member accounts. The cooperative also operates a transportation company called Western Transport. Western Transport provides transportation of anhydrous ammonia (NH3), liquid fertilizer (32-0-0 or 10-34-0), diesel, gasoline, and propane utilizing semi-tractors and trailers to Garden City Co-op, Inc. as well as to other agribusinesses in the region. The purpose of this thesis is to integrate and optimize the supply chain strategies for the cooperative’s fertilizer and petroleum products as it relates to storage and transportation of those commodities. Utilizing the framework of an aggregate production plan, a model is constructed to minimize costs associated with inventory holding, net storage asset depreciation after tax savings, net transportation asset depreciation after tax savings, labor, operations, and freight. By varying the quantities of petroleum and fertilizer the cooperative purchases, sells, and stores each month over a one-year period, an optimum mix of storage and transportation assets is determined. Two different demand scenarios are evaluated that relate to demand during a drought year versus demand during a non-drought year. Also, different model scenarios include varying beginning period inventory and ending period inventory to stress transportation assets versus storage assets. The model is optimized using a genetic algorithm solver in the software program Evolver produced by Palisade Corporation. Results of the optimization provided two feasible strategies for the cooperative. By continuing services to non-member accounts, there was a greater investment placed on transportation. Investments included additional trucks, NH3 trailers, petroleum trailers, and drivers. The strategy favored a just-in-time inventory approach versus inventory smoothing with storage. When discontinuing services to non-member accounts, investment between storage and transportation assets were relatively equal. The model favored a reduction in NH3 trailers, liquid fertilizer trailers, trucks, and drivers. However, additional storage was necessary as well as petroleum trailers. The scenario favored an inventory smoothing approach across the model year.

Doctor of Philosophy / Biochemistry and Molecular Biophysics Interdepartmental Program / John M. Tomich / David L. Wetzel / The technique of near infrared chemical imaging has been widely used for many industrial applications. It offers selectivity and/or sensitivity for numerous organic functional groups. The advantage of the near infrared spectroscopic region is the linear relationship of absorbance and concentration that enables quantitation. This universally employed technique has been a boon for research studies in the industrial process of wheat milling for the production of flour. The milling process has numerous sequential grinding and sieving steps that enable selective physical segregation of a starch rich endosperm product from wheat. Thousands of spectra of purified endosperm and non-endosperm standards are collected to develop a spectral library. Quantitation of the purity of individual processing streams is accomplished by applying a partial least squares calibration that is based upon the spectral library. The quantitative chemical imaging technique is useful for determination of endosperm purity profiles for mill flour streams. These plots reveal purity changes as less pure streams are added to produce a flour blend. The chemical structural basis furthermore allows comparison of purity even with changes in the wheat blend being milled with representative standardization. Furthermore, whereas a certain section of sieves is responsible, for designating the material defined as flour, application of the spectroscopic method is obvious. Select examples of key processing streams were studied to show the possibility of sieve-by-sieve analysis of the physical separation to provide mill optimization. These novel methods of analysis would not be possible without the sensitive and selective method of quantitative chemical imaging. Application of this technique to a few select unit processes is projected to reasonably affect a 1% increase in the yield of high quality flour. This amounts to a significant financial gain against low profit margins.

Simulated Annealing is a general purpose combinatorial optimization technique which has been applied to many problems in VLSI design. In essence, simulated annealing is Monte Carlo iterative improvement with the ability to conditionally accept uphill moves. The notion of a cooling schedule is common to all simulated annealing implementations. A cooling schedule can be thought of as simulated annealing's control mechanisms. Experiential work has been done on estimating the cost of an optimal solution to some combinatorial optimization problem instances. Such an estimate can be used to determine termination criteria for general purpose optimization techniques such as iterative improvement or simulated annealing. We have extended this idea and designed a complete simulated annealing general cooling schedule based on the cost of an optimal solution to a problem instance. We call the resultant schedule an extended goal-directed general cooling schedule. One of the major problems with simulated annealing is its long computation times. This problem can be addressed by first using a fast heuristic to find a good initial configuration and then applying simulated annealing. This approach is called Simulated Sintering. To exploit the potential of simulated sintering one needs an appropriate general cooling schedule. The extended goal-directed cooling schedule is equally applicable to simulated annealing and simulated sintering. To date, no one cooling schedule has proven suitable for all optimization problem instances. In our view, no such cooling schedule exits. Consequently, we have attempted to identify the type of problem best suited to optimization by simulated annealing and simulated sintering using the extended goal-directed schedule. We have applied the extended goal-directed schedule to standard-cell placement and floorplanning problems using both simulated annealing and simulated sintering. Within this context, we have compared the performance of the extended goal-directed schedule to other published schedules. Our results indicate that in terms of layout quality, the extended goal-directed schedule performs as well or better than the other schedules. In this dissertation, we have developed a new general cooling schedule. Our evaluation of the extended goal-directed schedule suggests that it is a useful research contribution in the area of simulated annealing algorithms. / Graduate

Resumo: Este projeto de pesquisa tem como objetivo a concepção, implementação, solução e teste de um modelo de Pré-Despacho de Geração (PDG) para o ambiente de mercados de energia, que supra as deficiências dos modelos de PDG adotados no Brasil. Assim, a abordagem proposta deve introduzir novos aspectos de modelagem, tais como: i) a introdução de aspectos associados aos mercados de energia internamente ao modelo de PDG; ii) a representação das inter-relações entre os mercados pool e bilateral em um único modelo de PDG; iii) a discretização do problema em base horária, possibilitando, de fato, a implementação de um mercado de curtíssimo prazo; iv) a avaliação da função de custo de oportunidade como base para a inserção de objetivos associados à otimização da produção de energia hidráulica no mercado pool / Abstract: This research aims at the conception, implementation, solution and testing of the proposed Short term Generation Scheduling Model (PDG), specific for the energy market environment. This model focuses on the improvement in the dispatch model used by the Brazilian energy sector. The proposed approach introduces brand new modeling aspects, such as: i) the introduction of modeling aspects associated with energy markets into the PDG model; ii) the representation of the interrelation between pool and bilateral markets within a single optimization problem; iii) the discretization of the problem is introduced in an hourly basis aiming at the implementation of an effective short time energy market; iv) the evaluation of the opportunity costs function as a basis for insertion of objectives associated with optimization of hydraulic energy production in pool market / Orientador: Leonardo Nepomuceno / Coorientador: Paulo Sérgio da Silva / Banca: Takaaki Ohishi / Banca: Edmea Cassia Baptista / Mestre

[pt] O presente trabalho aborda o problema da geração de energia a partir de combustíveis nucleares. È feito um estudo sobre a utilização conjunta de PWR (pressurized water reactors) e FBR (fast breeder reactors) num mesmo parque gerador nuclear. Mais especificamente, procura-se determinar a forma mais econômica (considerando os encargos anuais da geração) de se satisfazer um programa de implantação de capacidade em centrais nucleares pré-estabelecido, a partir da instalação de potência em PWR e FBR. / [en] This paper deals the problem of power generation with nuclear fuel. E study about utilization of PWR (pressurized water reactors) and FBR (fast breeder reactors) in a same nuclear park was done. More specifically, it try to find the most economical way (considering the annual costs of power generation) of to meet a pre-stabilished power program by PWR and FBR.

Structural optimization is an important field in engineering with a strong foundation on continuum mechanics, structural finite element analysis, computational techniques and optimization methods. Research in structural optimization of linear and geometrically nonlinear problems using the force method has not received appropriate attention by the research community. The present thesis constitutes a comprehensive study in the area of structural optimization. Development of new methodologies for analysis and optimization and their integration in finite element computer programs for analysis and design of linear and nonlinear structural problems are among the most important contributions. For linear problems, a force method formulation based on the complementary energy is proposed. Using this formulation, the element forces are obtained without the direct generation of the compatibility matrix. Application of the proposed method in structural size optimization under stress, displacement and frequency constraints has been investigated and its efficiency is compared with the conventional displacement formulation. Moreover, an efficient methodology based on the integrated force method is developed for topology optimization of adaptive structures under static and dynamic loads. It has been demonstrated that structural optimization based on the force method is computationally more efficient. For nonlinear problems, an efficient methodology has been developed for structural optimization of geometrical nonlinear problems under system stability constraints. The technique combines the nonlinear finite element method based on the displacement control technique for analysis and optimality criterion methods for optimization. Application of the proposed methodology has been investigated for shallow structures. The efficiency of the proposed optimization algorithms are compared with the mathematical programming method based on the Sequential Quadratic Programming technique. It is shown that structural design optimization based on the linear analysis for structures with intrinsic geometric nonlinearites may lead to structural failure. Finally, application of the group theoretic approach in structural optimization of geometrical nonlinear symmetric structures under system stability constraint has been investigated. It has been demonstrated that structural optimization of nonlinear symmetric structures using the group theoretic approach is computationally efficient and excellent agreement exists between the full space and the reduced subspace optimal solutions. / Graduate