This study was an extension of a study submitted in April 2014 by Sheldon D. Holt entitled “Ambient Temperature, Calf Intakes, and Weight Gains on Preweaned Dairy Calves”. A major component in a profitable dairy operation is the raising of female calves as replacement heifers; but since no direct income is generated by calf raising alone, it is often overlooked as a potential profit area on a dairy farm. Calf management practices that ultimately impact milk productivity and reproductive performance during a heifer’s lifetime begin at birth. This study examines the effect of calf starter intake on calf growth, measuring specifically calf weight. How calf starter intake affected production costs was also examined. Other factors included in the study were seasonal change, hip height, days since birth, and weather conditions. The cost of calf starter is one of the main contributors to total production cost in raising dairy calves. Since the amount of starter intake consumed by the calves in this study was measured by Holt, a cost analysis can be performed using these data. Therefore, the first two objectives of this study are to 1) develop a model which minimizes cost of starter feed (which is a variable controlled by the dairy producer) and 2) use the model developed under objective 1) to find the breakeven point (where the cost of an input is less than or equal to the value gained from that input) and conduct sensitivity analysis with respect to this point. Although an analysis was performed on the data at the close of its collection in 2014 by S.D Holt, there are several econometric issues that were not adequately addressed before these analyses were performed. The following problems have been found in the data: functional form, multicollinearity, heteroskedasticity, and serial correlation. Any interpretation or prediction based on these data, without these issues being resolved, is not reliable. In order for interpretations and predictions based on these data to be valid, the last two objectives of this study are to 3) define in detail the econometric problems that existed in Holt’s study and 4) find and implement solutions to econometric problems that existed in that study.

Moksliniame tyrime darbo autorius sukūrė efektyvų UAB „Levitelis“ pelno optimizavimo modelį. Įmonės „Levitelis“ efektyvus pelno optimizavimo modelis įvertina finansinės atskaitomybės pagrindinių rodiklių kitimą susijusį su ribiniu pelnu. Magistro darbe naudotasi tokiais tyrimo metodais: sisteminė analizė, operatyvinė analizė, retrospektyvi analizė, lyginimas, grafinis, grupavimo, naudojant duomenų dinamikos ir struktūros bei santykinių rodiklių analizė. Tyrimo tikslas buvo nustatyti UAB „Levitelis“ efektyvų pelno optimizavimo modelį. Šiame darbe pavyko pagrįsti pagrindinių makroekonominių rodiklių sąsajas su statybos šakos rodikliais ir su Lietuvos statybos įmone UAB „Levitelis“. Apibendrinus tyrimo rezultatus galima teigti, kad hipotezė pasitvirtino. / In the scientific survey the author has created the most effective model of profit optimization in the Joint Stock Company „ Levitelis“. The profit optimization model in „ Levitelis“ evaluates the alteration of the main indicators of financial accountability in connection with the limiting profit. Such research methods have been used in the study: systematic analysis, strategic analysis, retrospective analysis, comparison, graphic, grouping, using structure and relative indicators. The aim of the survey was to establish the most effective model of profit optimization in the Joint Sock Company „ Levitelis“. It was succeeded in substantiating the links of the main. Macro economical indicators with the indicators of building branch and with Lithuanian Joint Stock Company „ Levitelis“ Summarizing the results of the survey it can be asserted that the hypothesis was confirmed.

This research focuses on large-scale manufacturing systems having a number of stations with multiple tools and product types with different and deterministic processing steps. The objective is to determine the production quantities of multiple products and the tool requirements of each station that maximizes net profit while satisfying strategic constraints such as cycle times, required throughputs, and investment. The formulation of the problem, named OptiProfit, is a mixed-integer nonlinear programming (MINLP) with the stochastic issues addressed by mean-value analysis (MVA) and queuing network models. Observing that OptiProfit is an NP-complete, nonconvex, and nonmonotonic problem, the research develops a heuristic method, Differential Coefficient Based Search (DCBS). It also performs an upper-bound analysis and a performance comparison with six variations of Greedy Ascent Procedure (GAP) heuristics and Modified Simulated Annealing (MSA) in a number of randomized cases. An example problem based on a semiconductor manufacturing minifab is modeled as an OptiProfit problem and numerically analyzed. The proposed methodology provides a very good quality solution for the high-level design and operation of manufacturing facilities.

Submitted by Vinícius Bisogni (vibisogni@uol.com.br) on 2014-08-26T22:48:46Z No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) / Approved for entry into archive by JOANA MARTORINI (joana.martorini@fgv.br) on 2014-08-27T11:45:32Z (GMT) No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) / Made available in DSpace on 2014-08-27T12:01:56Z (GMT). No. of bitstreams: 1 Dissertacao_Vinicius de Araujo Bisogni.pdf: 2688252 bytes, checksum: 608dee9763c8c1a015d3b54b3692c5b0 (MD5) Previous issue date: 2014-07-30 / This paper aims to compare different methods of forecasting cash needs in overnight, to ensure that the liquidity of a particular financial product - in this case, the Call Deposits (Demand Deposits Account, in foreign currency) - are sufficient to cover the liquidity risks of a financial institution and, in other hand, optimize the profit provided from the remaining balance that exceeds the outputs of the models. Here, the Cash Flow model of Schmaltz (2009), which segregates the model in different components (deterministic and stochastic), is applied to determine the cash needs and, through the Monte Carlo method for predicting different cash flows, is stipulated an average value of balance to be used in overnight. As a contrast, the deterministic model of Ringbom et al (2004) is used to provide the "Profit-Maximizing Reserve Ratio" to finally compare both of them historically, between Jan/2009 and Dec/2013, in order to conclude which of models of cash reserve shows to be more satisfying. The database used replicate balances and withdraws of a commercial bank, to this specific financial product, and it is also used for parameters estimation. / Este trabalho tem como objetivo comparar diferentes métodos de previsão de necessidades de caixa no overnight, que assegurem que a liquidez de um produto financeiro específico – neste caso, o Call Deposits (Depósito à Vista, em moeda estrangeira) – seja suficiente para cobrir os riscos de liquidez de uma instituição financeira e, em contrapartida, otimizem o lucro oriundo do saldo restante que ultrapasse o valor de saída destes modelos. Para isso, o modelo de Fluxo de Caixa de Schmaltz (2009), que segrega os diferentes componentes do caixa (determinísticos e estocásticos), será utilizado para determinar as necessidades de caixa e, através do método de Monte Carlo para a previsão de diferentes caminhos, chegamos a um valor médio de saldo para ser utilizado no overnight. Como comparativo, será utilizado o modelo determinístico de Ringbom et al (2004), que oferece a 'Taxa de Reserva de Maximização de Lucro', para, enfim, compará-los historicamente, entre Jan/2009 e Dez/2013, a fim de concluirmos qual dos modelos de reservas de caixa se mostra mais satisfatório. A base de dados utilizada replica os saldos e saques de um banco comercial, para o produto financeiro em questão, e, também, é utilizada para a estimação dos parâmetros.

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L'informatique en nuage (Cloud Computing) est un modèle à grande échelle et en évolution continue, permettant le provisionnement et l'utilisation des ressources informatiques à la demande, selon un modèle rentable de facturation à l'usage "pay-as-you-go". Ce nouveau paradigme a rapidement révolutionné l'industrie IT et a permis de nouvelles tendances en matière de prestation de services informatiques, y compris l'externalisation des infrastructures IT vers des prestataires tiers spécialisés. Cependant, la nature multi-utilisateur des plateformes d'hébergement, ainsi que la complexité des demandes, soulèvent plusieurs défis liés à la gestion des ressources Cloud. Malgré l'attention croissante portée à ce sujet, la plupart des efforts ont été axés sur des solutions centrées utilisateur, et malheureusement beaucoup moins sur les difficultés rencontrées par les fournisseurs pour maximiser leurs bénéfices. Dans ce contexte, la fédération de Cloud a été récemment proposée comme une solution clé pour répondre à l'augmentation et la fluctuation des charges de travail. Les fournisseurs ayant des besoins complémentaires en ressources au fil du temps, peuvent collaborer et partager leurs infrastructures respectives via l'externalisation ("Outsourcing") pour mieux satisfaire les demandes et exigences des utilisateurs. Cette thèse aborde le problème d'optimisation du profit via la fédération et l'allocation optimale des ressources parmi plusieurs fournisseurs d'infrastructures Cloud. L'étude examine les principaux défis et opportunités liés à la maximisation des revenus dans une fédération de Clouds, et définit des stratégies efficaces pour diriger les fournisseurs dans leurs décisions de coopération. Le but est de fournir des algorithmes qui automatisent la sélection du plan d'allocation le plus rentable, qui satisfait à la fois la demande des utilisateurs et les exigences de mise en réseau. Nous visons des modèles d'allocation génériques et robustes qui répondent aux nouvelles tendances Cloud, et de traiter les requêtes simples ainsi que complexes nécessitant le provisionnement de services composites avec différentes ressources distribuées et connectées. Conformément aux objectifs de la thèse, nous avons mené une étude approfondie des travaux antérieurs traitant la problématique de provisionnement des ressources d'infrastructure dans les environnements Cloud. L'analyse a porté notamment sur les modèles d'allocation ayant pour objectif la maximisation de profit et les lacunes et défis associés dans les fédérations de Clouds. Dans un deuxième temps, nous avons proposé un programme linéaire en nombre entiers (ILP), pour aider les fournisseurs de services dans leurs décisions de coopération via des actions optimales d'externalisation (outsourcing), d'internalisation (insourcing) et d'allocation en local. Ces différentes décisions d'allocation sont traitées conjointement dans une formule d'optimisation globale qui partitionne les graphes de requêtes entre les membres de la fédération, tout en satisfaisant les exigences de communication entre les services élémentaires. En plus de la topologie des graphes de ressources, ce partitionnement prend en compte les prix dynamiques et les quotas proposés par les membres de la fédération ainsi que les coûts d'hébergement des ressources et de leur mise en réseau. Enfin, nous avons proposé un algorithme heuristique pour améliorer les temps de convergence avec les instances de problèmes à grande échelle. L'approche proposée utilise un algorithme de "clustering" basé sur les arbres de Gomory-Hu pour le partitionnement des graphes et une stratégie de meilleur ajustement (Best-Fit matching) pour l'allocation et le placement des sous-graphes résultants. L'utilisation conjointe de ces deux techniques permet de capturer l'essence du problème d'optimisation et de respecter les différents objectifs fixés, tout en améliorant le temps de convergence vers les solutions quasi-optimales de plusieurs ordres de grandeur / Cloud computing is a steadily maturing large-scale model for providing on-demand IT resources on a pay-as-you-go basis. This emerging paradigm has rapidly revolutionized the IT industry and enabled new service delivery trends, including infrastructure externalization to large third-party providers. The Cloud multi-tenancy architecture raises several management challenges for all stakeholders. Despite the increasing attention on this topic, most efforts have been focused on user-centric solutions, and unfortunately much less on the difficulties encountered by Cloud providers in improving their business. In this context, Cloud Federation has been recently suggested as a key solution to the increasing and variable workloads. Providers having complementary resource requirements over time can collaborate and share their respective infrastructures, to dynamically adjust their hosting capacities in response to users' demands. However, joining a federation makes the resource allocation more complex, since providers have to also deal with cooperation decisions and workload distribution within the federation. This is of crucial importance for cloud providers from a profit standpoint and especially challenging in a federation involving multiple providers and distributed resources and applications. This thesis addresses profit optimization through federating and allocating resources amongst multiple infrastructure providers. The work investigates the key challenges and opportunities related to revenue maximization in Cloud federation, and defines efficient strategies to govern providers' cooperation decisions. The goal is to provide algorithms to automate the selection of cost-effective distributed allocation plans that simultaneously satisfy user demand and networking requirements. We seek generic and robust models able to meet the new trends in Cloud services and handle both simple and complex requests, ranging from standalone VMs to composite services requiring the provisioning of distributed and connected resources. In line with the thesis objectives, we first provide a survey of prior work on infrastructure resource provisioning in Cloud environments. The analysis mainly focuses on profit-driven allocation models in Cloud federations and the associated gaps and challenges with emphasis on pricing and networking issues. Then, we present a novel exact integer linear program (ILP), to assist IaaS providers in their cooperation decisions, through optimal "insourcing", "outsourcing" and local allocation operations. The different allocation decisions are treated jointly in a global optimization formulation that splits resource request graphs across federation members while satisfying communication requirements between request subsets. In addition to the request topology, this partitioning takes into account the dynamic prices and quotas proposed by federation members as well as the costs of resources and their networking. The algorithm performance evaluation and the identified benefits confirm the relevance of resource federation in improving providers' profits and shed light into the most favorable conditions to join or build a federation. Finally, a new topology-aware allocation heuristic is proposed to improve convergence times with large-scale problem instances. The proposed approach uses a Gomory-Hu tree based clustering algorithm for request graphs partitioning, and a Best-Fit matching strategy for subgraphs placement and allocation. Combining both techniques captures the essence of the optimization problem and meets the objectives, while speeding up convergence to near-optimal solutions by several orders of magnitude

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