O processo de tomada de decisão que envolve a priorização e a seleção de projetos de investimentos na indústria do petróleo está longe de ser uma tarefa trivial. Ao mesmo tempo em que a empresa deve buscar relações favoráveis entre risco e retorno econômico-financeiro também deve se alinhar cada vez mais aos princípios do desenvolvimento sustentável em seus negócios. Em se tratando do caso da indústria petrolífera brasileira, formada essencialmente por um monopólio estatal, esta tarefa se torna ainda mais difícil, já que uma série de interesses públicos relacionados ao investimento também devem ser considerados. Sendo assim, o objetivo principal desta pesquisa foi o desenvolvimento e a aplicação de um modelo original de análise usando múltiplos critérios que auxiliasse na priorização e na seleção de projetos de investimentos nas refinarias de petróleo brasileiras. Utilizou-se uma metodologia de pesquisa quantitativa com o uso de diversos artefatos de matemática aplicada capazes de lidar adequadamente com as avaliações muitas vezes incompletas e subjetivas que caracterizam o problema da análise de investimentos em refinarias de petróleo. Ao final do trabalho, conseguiu-se obter um modelo suficientemente simples, ao ponto de ser facilmente implementado em uma planilha eletrônica, robusto, ao ser capaz de lidar de maneira bastante adequada com as principais peculiaridades que envolvem o setor do refino de petróleo no Brasil e flexível, de maneira que os critérios de análise e as alternativas de decisão pudessem ser facilmente adicionados, removidos ou alterados de acordo com as necessidades específicas exigidas para cada caso. / The decision-making process involving the prioritization and selection of investment projects in the oil industry is far from being a trivial task. At the same time that the company must seek for a favorable relationship between risk and economic-financial return it must also be more and more aligned to the principles of sustainable development in their business. Regarding to the case of the Brazilian oil industry, essentially formed by a state monopoly, this task becomes even more difficult, since a number of public interests related to investment should also be considered. Thus, the main objective of this research was the development and application of an original analysis model using multiple criteria that help in the prioritization and selection of investment projects on the Brazilian oil refineries. We used a quantitative research methodology using various applied mathematical tools capable of dealing properly with often incomplete and subjective valuations of investment analysis in oil refineries. At the end, it was possible to obtain a sufficiently simple model, that could be easily implemented in a spreadsheet, robust, to deal in a properly way with the main peculiarities involving the oil refining sector in Brazil and sufficiently flexible so that the criteria for analysis and decision alternatives could be easily added, removed or modified in accordance with the specific needs required for each case.

An organization’s information technology (IT) infrastructure capability is increasinglyrealized as a critical part to business effectiveness and efficiency. IT infrastructure servicesare particularly important for organizations looking to deploy business processes indeveloping markets. There has also been an interest from many small and medium sizedorganizations whose core business is not in IT to outsource and manage these servicesthrough third party service providers. However there is a need to create an understanding forthese organizations to deploy the right infrastructure services in order to enable easierimplementation or reengineering of the business process. There has been little researchfocusing on the patterns of the IT infrastructure capabilities in the small and medium sizedorganizations in the developing markets.The research aims for a comprehensive coverage by analyzing the requirements in thedeveloping markets and proposing a selection model for the organizations to choose ITservice provider in case they decide to outsource the infrastructure services. The effect of theIT infrastructure services on the business process implementation is presented with anemphasis on the boundary crossing services. Using empirical case study, the research analysesa firm in developing markets and compares it against four strategically similar organizationsfrom different industries. Data collection was primarily qualitative and ably supported bysecondary data.The requirements in developing markets reflect the same as in mature markets. The pricing isseen to play a major role in the selection of the service providers with service security notvery much organization’s priority. The number of boundary crossing services effectivelyenables information sharing and control. These services are the drivers in simplifying thebusiness process implementation. The findings have implications for both business andtechnical managers in regard to planning the IT strategy in the long term and developingappropriate infrastructure according to the process needs. / Program: Magisterutbildning i informatik

Innovation is one of the most important sources of competitive advantage. It helps a company to fuel the growth of new products and services, sustain incumbents, create new markets, transform industries, and promote the global competitiveness of nations. Because of its importance, companies need to manage innovation. It is very important for a company to be able to measure its innovativeness because one cannot effectively manage without measurement. A good measurement model will help a company to understand its current capability and identify areas that need improvement. In this research a systematic approach was developed for a company to measure its innovativeness. The measurement of innovativeness is based on output indicators. Output indicators are used because they cannot be manipulated. A hierarchical decision model (HDM) was constructed from output indicators. The hierarchy consisted of three levels: innovativeness index, output indicators and sub-factors. Experts' opinions were collected and quantified. A new concept developed by Dr. Dundar Kocaoglu and referred to as "desirability functions" was implemented in this research. Inconsistency of individual experts, disagreement among experts, intraclass correlation coefficients and statistical F-tests were calculated to test the reliability of the experts' judgments. Sensitivity analysis was used to test the sensitivity of the output indicators, which indicated the allowable range of the changes in the output indicators in order to maintain the priority of the sub-factors. The outcome of this research is a decision model/framework that provides an innovativeness index based on readily measurable company output indicators. The model was applied to product innovation in the technology-driven semiconductor industry. Five hypothetical companies were developed to simulate the application of the model/framework. The profiles of the hypothetical companies were varied considerably to provide a deeper understanding of the model/framework. Actual data from two major corporations in the semiconductor industry were then used to demonstrate the application of the model. According to the experts, the top three sub-factors to measure the innovativeness of a company are revenue from new products (28%), market share of new products (21%), and products that are new to the world (20%).

Many entrepreneurs and companies use loans to cover their business needs. Usually it is difficult to choose the best offer. The possible solution is the utilization of methods of multiple-criteria decision-making, which make the decision process easier. The goal of this thesis is to describe these methods and use them practically to choose the best loan offer. It was found out that most of the companies do not use these methods. The results are usually significantly affected by the criterion which was the most preferred. For the most of the companies the offer made by MONETA Money Bank, a.s. was the most favourable.

Dealing with optimization problems with more than one objective has been an important research area in evolutionary computation. The class of multi-objective problems (MOPs) is an important one because multi-objectivity exists in almost all aspects of human life; whereby there usually exist several compromises in each problem. Multi-objective evolutionary algorithms (MOEAs) have been applied widely in many real-world problems. This is because (1) they work with a population during the course of action, which hence offer more flexible control to find a set of efficient solutions, and (2) real-world problems are usually black-box where an explicit mathematical representation is unknown. However, MOEAs usually require a large amount of computational effort. This is a sub- stantial challenge in bringing MOEAs to practice. This thesis primarily aims to address this challenge through an investigation into issues of scalability and the balance between exploration and exploitation. These have been outstanding research challenges, not only for MOEAs, but also for evolutionary algorithms in general. A distributed framework of local models using explicit niching is introduced as an overarching umbrella to solve multi-objective optimization problems. This framework is used to address the two-part question about first, the role of communication messages and second, the role of explicit niching in distributed evolutionary multi-objective optimization. The concept behind the framework of local models is for the search to be conducted locally in different areas of the decision search space, which allows the local models to be distributed on different processing nodes. During the optimization process, local models interact (exchange messages) with each other using rules inspired from Particle Swarm Optimization (PSO). Hence, the hypothesis of this work is that running simultaneously several search engines in different local areas is better for exploiting local information, while exchanging messages among those diverse engines can provide a better exploration strategy. For this framework, as the models work locally, they gain access to some global knowledge of each other. In order to validate the proposed framework, a series of experiments on a wide range of test problems was conducted. These experiments were motivated by the following studies which in their totality contribute to the verification of our hypothesis: (1) studying the performance of the framework under different aspects such as initialization, convergence, diversity, scalability, and sensitivity to the framework's parameters, (2) investigating interleaving guidance in both the decision and objective spaces, (3) applying local models using estimation of distributions, (4) evaluating local models in noisy environments and (5) the role of communication messages and explicit niching in distributed computing. The experimental results showed that: (1) the use of local models increases the chance of MOEAs to improve their performance in finding the Pareto optimal front, (2) interaction strategies using PSO rules are suitable for controlling local models, and that they also can be coupled with specialization in order to refine the obtained non-dominated set, (3) estimation of distribution improves when coupled with local models, (4) local models work well in noisy environments, and (5) the communication cost in distributed systems with local models can be reduced significantly by using summary information (such as the direction information naturally determined by local models) as the communication messages, in comparison with conventional approaches using descriptive information of individuals. In summary, the proposed framework is a successful step towards efficient distributed MOEAs.

The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.

The purpose of this thesis is to develop a model for assessing cost effectiveness of applying lean tools. The model consists of eight phases: it starts by understanding customers' requirements using Voice of Customer (VOC) and Quality Function Deployment (QFD) tools. In phase 2, the current state of plant is assessed using lean profile charts based on Balanced Scorecard (BSC) measures. In phase 3 and phase 4, identification of critical problem(s) and generating of improvement suggestion(s) are performed. Phase 5 provide evaluation of the cost effectiveness of implementing the suggested lean methods based on life cycle cost analysis (LCCA) and phase 6 prefers the right alternative based on multiple criteria decision making (MCDM). In phase 7 the selected alternative is supposed to be implemented and finally the user should monitor and control the process to make sure that the improvement is going as planned. The model was verified successfully using a case study methodology at one Swedish sawmill called Södra Timber in Ramkvilla, one part of Södra group. Results obtained from the study showed that the production and human resources perspectives are the most critical problem areas that need to be improved. They got the lowest scores in the lean profile, 63% and 68%, respectively. Using value stream mapping (VSM) it was found that the non value added (NVA) ratios for the core and side products are 87.4% and 90.4%, respectively. Using the model, three improvement alternatives were suggested and evaluated using LCCA and MCDM. Consequently, implementing 5S got the highest score, second came redesigning the facility layout. However, it was estimated that 4.7 % of NVA for the side product would be reduced by redesigning the facility layout. The recommendations were suggested for the company to improve their performance. The novelty of the thesis is based on the fact that it addresses two main issues related to lean manufacturing: firstly, suggesting lean techniques based on assessment of lean profile that is based on BSC and QFD, and secondly assessing the cost effectiveness of the suggested lean methods based on LCCA and MCDM. This thesis provides a generalized model that enables the decision-maker to know and measure, holistically, the company performance with respect to customer requirements. This will enable the company to analyze the critical problems, suggest solutions, evaluate them and make a cost effective decision. Thus, the company can improve its competitiveness.

This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space. Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each objective, the computational expense grows with each additional objective. This method has developed methods that reduces the time required for evolution by reducing the number of objective evaluations, while still evolving solutions that are Pareto optimal. To date, all other Multiple Objective Evolutionary Algorithms (MOEAs) require the evaluation of all objectives before a fitness value can be assigned to an individual. The original contributions of this thesis are: 1. Development of a hierarchical search space description that allows association of crossover and mutation settings with elements of the genotypic description. 2. Development of a method for parallel evaluation of individuals that removes the need for delays for synchronization. 3. Dynamical evolution of thresholds for objectives to allow partial evaluation of objectives for individuals. 4. Dynamic objective orderings to minimize the time required for unnecessary objective evaluations. 5. Application of MOEAs to the computationally expensive flare pattern design domain. 6. Application of MOEAs to the optimization of fielded missile warning receiver algorithms. 7. Development of a new method of using MOEAs for automatic design of pattern recognition systems.

A paradigm shift is underway in which the classical materials selection approach in engineering design is being replaced by the design of material structure and processing paths on a hierarchy of length scales for specific multifunctional performance requirements. In this dissertation, the focus is on designing mesoscopic material and product topology?? geometric arrangement of solid phases and voids on length scales larger than microstructures but smaller than the characteristic dimensions of an overall product. Increasingly, manufacturing, rapid prototyping, and materials processing techniques facilitate tailoring topology with high levels of detail. Fully leveraging these capabilities requires not only computational models but also a systematic, efficient design method for exploring, refining, and evaluating product and material topology and other design parameters for targeted multifunctional performance that is robust with respect to potential manufacturing, design, and operating variations. In this dissertation, the Robust Topological Preliminary Design Exploration Method is presented for designing complex multi-scale products and materials by topologically and parametrically tailoring them for multifunctional performance that is superior to that of standard designs and less sensitive to variations. A comprehensive robust design method is established for topology design applications. It includes computational techniques, guidelines, and a multiobjective decision formulation for evaluating and minimizing the impact of topological and parametric variation on the performance of a preliminary topological design. A method is also established for multifunctional topology design, including thermal topology design techniques and multi-stage, distributed design methods for designing preliminary topologies with built-in flexibility for subsequent modification for enhanced performance in secondary functional domains. Key aspects of the approach are demonstrated by designing linear cellular alloys??ered metallic cellular materials with extended prismatic cells?? three applications. Heat exchangers are designed with increased heat dissipation and structural load bearing capabilities relative to conventional heat sinks for microprocessor applications. Cellular materials are designed with structural properties that are robust to dimensional and topological imperfections such as missing cell walls. Finally, combustor liners are designed to increase operating temperatures and efficiencies and reduce harmful emissions for next-generation turbine engines via active cooling and load bearing within topologically and parametrically customized cellular materials.

iv In this study, an Analytic Network Process (ANP) and Data Envelopment Analysis (DEA) based approach was developed in order to measure the performance of customer-based Research and Development projects being executed in T&Uuml / BTAKSAGE, Defense Research and Development Institute, under the Scientific and Technological Research Council of Turkey. In order to evaluate project performance, many criteria, containing various subcriteria were determined. In order to handle the interdependencies among the criteria and the sub-criteria, ANP was used. The ANP model generated in this study is a hybrid model consisting of both a hierarchy and a network. The pairwise comparison matrices that were built up for defining the importance and influences of the criteria/sub-criteria in the ANP model were formed as interval judgments from a group decision making process, based on data obtained from a questionnaire conducted among the experts in the Institute. From the interval pairwise comparison matrices, weight intervals for the sub-criteria were determined and these bounds were used as assurance region constraints in a super-efficiency DEA model, through which the project ranking was obtained. Taking into consideration that there may occur some missing values in some projects for some of the sub-criteria, the superefficiency DEA model was extended to handle missing data. The model was applied to a real case study on performance evaluation of the ongoing customer-based projects in the Institute. For comparison purposes, the case study was also solved by two other approaches.