In this thesis, identification of periodically varying orders of univariate Periodic Autoregressive Moving-Average (PARMA) processes is mainly studied. The identification of the varying orders of PARMA process is carried out by generalizing the well-known Box-Jenkins techniques to a seasonwise manner. The identification of pure periodic moving-average (PMA) and pure periodic autoregressive (PAR) models are considered only. For PARMA model identification, the Periodic Autocorrelation Function (PeACF) and Periodic Partial Autocorrelation Function (PePACF), which play the same role as their ARMA counterparts, are employed. For parameter estimation, which is considered only to refine model identification, the conditional least squares estimation (LSE) method is used which is applicable to PAR models. Estimation becomes very complicated, difficult and may give unsatisfactory results when a moving-average (MA) component exists in the model. On account of overcoming this difficulty, seasons following PMA processes are tried to be modeled as PAR processes with reasonable orders in order to employ LSE. Diagnostic checking, through residuals of the fitted model, is also performed stating its reasons and methods. The last part of the study demonstrates application of identification techniques through analysis of two seasonal hydrologic time series, which consist of average monthly streamflows. For this purpose, computer programs were developed specially for PARMA model identification.

In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map. The developed algorithm is tested on 8-band WorldView-2 satellite images. It is observed that, the proposed road extraction algorithm yields 47 % precision and 70 % recall. The approach is also tested on the lower spectral resolution images with four-band, RGB and gray level. It is observed that the additional four bands provide an improvement of 12 % for precision and 3 % for recall. Road type analysis is also in the scope of this study. Roads are classified into asphalt, concrete and unpaved using Gaussian Mixture Models. Other linear objects such as railroads and water canals may also be extracted by this process. An algorithm that classifies drive roads and railroads for very high resolution images is also investigated. It is based on the Fourier descriptors that identify the presence of railroad sleepers. Water canals are also extracted in multi-spectral images by using spectral ratios that employ the near infrared bands. Structural properties are used to distinguish water canals from other water bodies in the image.

Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures / incorporate a priori information such as topographic constraints, road maps as much as possible / use enhanced gating techniques to minimize the eect of clutter / develop methods against stop-move motion and hide motion of the target / tackle on-road/o-road transitions and junction crossings / establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.

In this study, we propose two biobjective mathematical models based on PROMETHEE V method for project selection problems. We develop an interactive approach (ib-PROMETHEE V) including data mining techniques to solve the first proposed mathematical model. For the second model, we propose NSGA-II with constraint handling method. We also develop a Preference Based Interactive Multiobjective Genetic Algorithm (IMGA) to solve the second proposed mathematical model. We test the performance of NSGA-II with constraint handling method and IMGA on randomly generated test problems.

Inventory management and production control problem of a dealer operating in a decentralized spare parts network is analyzed in this dissertation. Spare parts network is assumed to be formed of two dealers and the problem of a dealer is considered under the assumption that the other dealer has a known policy. These dealers collaborate through inventory and service pooling. Furthermore, the dealers collaborate through sharing information on the net inventory status. Upon demand arrival, a dealer may request a part from the other dealer, in which case a payment is made. Under this competitive and collaborative environment, the optimal operating policy of an individual dealer is characterized under full information. Through computational analysis, the conditions under which the dealer under consideration is most profitable are identified. Finally, by comparing dierent pooling strategies and several information availability levels, the benefit of information sharing is quantified.

In this study, the capacitated vehicle routing problem with shift based time constraints is taken into consideration. The study stemmed from an application in a cross dock. The considered cross dock is assumed to feed directly the production lines of its customer. The customer has a just-in-time production system that requires producing only in necessary quantities at the necessary times. This necessitates the arrival of the parts/products collected from different suppliers at the customer at the beginning of each shift of production. The shift times constitute deadlines for the products to be collected from the suppliers and used in each shift. The collection problem then can be seen as the capacitated vehicle routing problem with shift based time constraints. The objective of the collection problem is to minimize the routing costs. For the accomplishment of this objective it is required to decide on products of which shift(s) should be taken from a supplier when a vehicle arrives at that supplier. For the solution of the problem a mathematical model is formulated. Since the dealt problem is NP-Hard, meta-heuristic solution approaches based on variable neighborhood search and simulated annealing are proposed. Computational experimentation is conducted on the test problems which are tailored from the capacitated vehicle routing instances from the literature.

Optimization theory is a key technology for inverse problems of reconstruction in science, engineering and economy. Discrete tomography is a modern research field dealing with the reconstruction of finite objects in, e.g., VLSI chip design, where this thesis will focus on. In this work, a framework with its supplementary algorithms and a new problem reformulation are introduced to approximately resolve this NP-hard problem. The framework is modular, so that other reconstruction methods, optimization techniques, optimal experimental design methods can be incorporated within. The problem is being revisited with a new optimization formulation, and interpretations of known methods in accordance with the framework are also given. Supplementary algorithms are combined or incorporated to improve the solution or to reduce the cost in terms of time and space from the computational point of view.

Radar signal processing techniques aim to suppress clutter to enable target detection. Many clutter suppression techniques have been developed to improve the detection performance in literature. Among these methods, the most widely known is MTI plus coherent integrator, which gives sufficient radar performance in various scenarios. However, when the correlation coefficient of clutter is small or the spectral separation between the target and clutter is small, classical approaches to clutter suppression fall short. In this study, we consider the ARMA spectral estimation performance in sea clutter modelled by compound K-distribution through Monte Carlo simulations. The method is applied for varying conditions of clutter spikiness and auto correlation sequences (ACS) depending on the radar operation. The performance of clutter suppression using ARMA spectral estimator, which will be called ARMA-CS in this work, is analyzed under varying ARMA model orders. To compare the clutter suppression of ARMA-CS with that of conventional methods, we use improvement factor (IF) which is the ratio between the output Signal to Interference Ratio (SIR) and input SIR as performance measure. In all cases, the performance of ARMA-CS method is better than conventional clutter suppression methods when the correlation among clutter samples is small or the spectral separation between target and clutter is small.