We propose a generic algorithm based on branch-and-bound to generate all efficient solutions of multiobjective combinatorial optimization (MOCO) problems. We present an algorithm specific to multiobjective 0-1 Knapsack Problem based on the generic algorithm. We test the performance of our algorithm on randomly generated sample problems against IBM ILOG CPLEX and we obtain better performance using a problem specific algorithm. We develop a heuristic algorithm by incorporating memory limitations at the expense of solution quality to overcome memory issues of the exact algorithm.

Due to the economic and social growth of Turkey, there is a rapid increase in energy demand. Turkey does not have sufficient natural gas and petroleum reserves / however, it has large hydropower potential. Hydropower is the most widely used form of renewable energy. To generate electricity between elevations 1500 m and 450 m on Balkusan Creek, General Directorate of Electrical Power Resources Survey and Development Administration (EIEI) conducted a feasibility study for a hydroelectric power plant (HEPP) composed of a rock-fill dam in 1999. In 2009, this formulation was not only revised but also constructed by Hidromark Company. The Hidromark-formulation consists of a roller compacted concrete dam and two diversion weirs. The aim of this study is to find a more beneficial alternative to the previous formulations. In order to avoid dam body and expropriation costs the Alternative-formulation composed of a run-of-river HEPP is developed. In the economic analysis, HEPPs are assumed to have two equal sized turbines and net benefits of different formulations are compared. Additionally, energy generation calculations are carried out for two turbines with different installed capacities only for the Alternative-formulation in order to investigate impact of turbine size on energy generation.

In this study, it is aimed to determine and implement a process based strategic planning methodology for Turkish Ministry of Finance. Proposed methodology has been implemented in Ministry of Finance by a T&Uuml / BiTAK supported strategic management project in collaboration with Ministry of Finance Strategy Development Unit (MoF_SDU), T&Uuml / SSiDE and a private Consultancy Company. This project was a direct consequence of reforms on Public Financial Management and stipulated by the Laws No. 5018 and 5436 which have been prepared based on efficiency in public service delivery. Proposed methodology is different from other strategic planning methodologies for public and non-profit organizations because it uses a combination of numerous techniques that are previously developed for strategic planning. Examples are mentoring, e- learning, idea tray method, and process modelling. Firstly, strategic planning methodology is determined with close inspection of international approaches and best practices. Ministry of Finance&rsquo / s organizational chart is redesigned to enable more effective and efficient operation. Ministry of Finance is divided to 14 strategic business units (SBUs). For every SBU, detailed environmental analyses are conducted and critical issues are derived. Mission and strategic vision statements are clearly determined and core processes are modelled and standardized. Strategic goals and detailed objectives are formulated. Finally, performance indicators are determined for every objective for evaluation and continuous improvement. At the end of the implementation, 2008-2012 strategic plan for Ministry of Finance is prepared and numerous organizational improvements are observed.

This thesis is about diagrammatic reasoning and error-finding in conceptual modeling diagrams. Specifically, the differences of the cognitive strategies and behaviors of notation-familiar participants versus domain-familiar participants working on conceptual modeling diagrams are inspected. The domain-familiar participants are experienced in the topic being represented, but they do not have any formal training in software development representations. On the other hand, the notation-familiar participants are educated in software representations, but unfamiliar with the topic represented. The main experiment and the follow-up experiment also aim to study how some properties of diagrams affect the error-finding behaviors. The participant groups&rsquo / performances in the main experiment are investigated and compared by the analysis of verbal protocol data and eye movement data. The combination of the two different methods enhances detailed analyses. In the follow-up experiment, only eye movement data is involved to evaluate how some properties of diagrams affect problem-solving. By means of both experiments, it is concluded that diagrammatic complexity has a negative effect on reasoning whereas the degree of causal chaining improves diagrammatic reasoning. In the main experiment, some differences in the diagrammatic reasoning processes between the groups are observed, too. The notation-familiar participants are observed to be more successful in error-finding although they are unfamiliar with the topic. This study underlines the interaction of cognitive science and software engineering by integrating eye movement data, verbal protocol analysis and performance data into the cognitive inspection of software engineering notations.

A software process improvement study is presented. The literature on software development processes and their improvement is reviewed. The current peer review process at Software Engineering Directorate of the X Company, Ankara, T&uuml / rkiye (XCOM) is studied and the static software development metrics based on a recent proposal have been evaluated. The static software metrics based improvement suggestions and the author&rsquo / s improvement suggestions discussed with the senior staff are compared. An improved peer review process is proposed. The static software development metrics have been evaluated on the improved process to see the impacts of the improvements. The improved process has been already implemented at XCOM and preliminary results have been obtained.

Active learning has proven to be a successful strategy in quick development of corpora to be used in statistical induction of natural language. A vast majority of studies in this field has concentrated on finding and testing various informativeness measures for samples / however, representativeness measures for samples have not been thoroughly studied. In this thesis, we introduce a novel representativeness measure which is, being based on Zipf&#039 / s law, model-independent and validated both theoretically and empirically. Experiments conducted on WSJ corpus with a wide-coverage parser show that our representativeness measure leads to better performance than previously introduced representativeness measures when used with most of the known informativeness measures.

Web usage mining has become popular in various business areas related with Web site development. In Web usage mining, the commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications. The semantic information of the Web page contents is generally not included in Web usage mining. In this thesis, a framework for integrating semantic information with Web usage mining is implemented. The frequent navigational patterns are extracted in the forms of ontology instances instead of Web page addresses and the result is used for making page recommendations to the visitor. Moreover, an evaluation mechanism is implemented to find the success of the recommendation. Test results proved that stronger and more accurate recommendations are obtained by including semantic information in the Web usage mining instead of using on visited Web page addresses.

In this thesis, we consider a Disassembly Line Balancing Problem (DLBP) with fixed number of workstations. We aim to maximize the total value of the recovered parts. We assume that there is a limited supply for the products to be disassembled. Different components can be obtained by disassembling different units of the product. Our aim is to assign the tasks to the workstations of the disassembly line so as to maximize the total value of the recovered parts. We present several upper and one lower bounding procedure. The results of our computational study have revealed the satisfactory behavior of our bounding mechanisms.

In this study, we address the long-term project pricing problem for a company that operates in the defense industry. The pricing problem is a bid project pricing problem which includes various technical and financial uncertainties, such as estimations of workhour content of the project and exchange &amp / inflation rates. We propose a Robust Optimization (RO) approach that can deal with the uncertainties during the project lifecycle through the identification of several discrete scenarios. The bid project&rsquo / s performance measures, other than the monetary measures, for R&amp / D projects are identified and the problem is formulated as a multi-attribute utility project pricing problem. In our RO approach, the bid pricing problem is decomposed into two parts which are v solved sequentially: the Penalty-Model, and the RO model. In the Penalty-Model, penalty costs for the possible violations in the company&rsquo / s workforce level due to the bid project&rsquo / s workhour requirements are determined. Then the RO model searches for the optimum bid price by considering the penalty cost from the Penalty-Model, the bid project&rsquo / s performance measures, the probability of winning the bid for a given bid price and the deviations in the bid project&rsquo / s cost. Especially for the R&amp / D type projects, the model tends to place lower bid prices in the expected value solutions in order to win the bid. Thus, due to the possible deviations in the project cost, R&amp / D projects have a high probability of suffering from a financial loss in the expected value solutions. However, the robust solutions provide results which are more aware of the deviations in the bid project&rsquo / s cost and thus eliminate the financial risks by making a tradeoff between the bid project&rsquo / s benefits, probability of winning the bid and the financial loss risk. Results for the probability of winning in the robust solutions are observed to be lower than the expected value solutions, whereas expected value solutions have higher probabilities of suffering from a financial loss.

This study addresses sorting problems with predefined ordinal classes. We develop a new method based on Support Vector Machine (SVM) model, which is mainly used for nominal binary or multi-class classification processes. In the proposed method, the SVM model is extended to include the preferences of the decision maker and the ordinal relationship between classes in sorting problems. New sets of constraints are added to the SVM model. We demonstrate the performance of the proposed method through several data sets. We compare the results with those of classical SVM model and UTADIS method, a well-known multiple criteria sorting method. We also analyze the effect of feature space mapping by Kernel Trick utilization on the results.