The most important aim of pre-positioning is to reduce the delivery lead time with eliminating the procurement stage by positioning items closer to the disaster area. The last 30 years&rsquo / data is used to designate the disaster trends / EM-DAT database is used to acquire the necessary data which includes the disaster locations, type of disasters and number of people affected. Also the most recent four years&rsquo / data is used for verification of the results. Locations of the optimal warehouses for pre-positioning are determined considering the generated emergency response scenarios. When we pursue this exploration, besides determining the optimal pre-positioning locations given by CARE International, we also determined where the natural disaster trend drifts towards.Therefore, this research tries to find an answer whether the disaster trends should be considered to determine the location of the pre-positioned items or not.

In this study / reliability and performance of turbulence models used in CFD softwares to determine the aerodynamics of passenger cars, are tested and compared. In the analyses, drag forces acting on the car, pressure and velocity distributions and wake flow patterns are determined by using several turbulence models with a commercial software Fluent. Calculated results compared to the experimental results given in the literature. It is observed that, turbulence models give relatively reliable results for determining aerodynamic properties of the model car. Among the turbulence models, RNG k-&epsilon / and standard k-&omega / models stand one step ahead of the other models according to results.

In this thesis we consider a Time-Dependent Vehicle Routing Problem with Time Windows (TDVRPTW) which is solved by a Branch and Cut and Price (BCP) algorithm. The decomposition of an arc based formulation leads to a set-partitioning problem as the master problem, and a Time-Dependent Elementary Shortest Path Problem with Resource Constraints (TDESPPRC) as the pricing problem. The main contribution of this thesis is the modified fathoming and bounding procedures applied on bi-directional Time-Dependent Labeling algorithm (TDL) which is used solve the TDESPPRC. The aim of the fathoming proposed is to solve TDVRPTW more efficiently by not extending the unproductive labels in bi-directional TDL algorithm. Moreover, an arc bounding model is introduced to stop the extension of labels as an alternative to resource bounding used in bi-directional search. In addition, independent from the work on TDVRPTW, the thesis includes an effects analysis of a new customer on Kuehne+Nagel(K+N) Netherlands Fast Moving Consumer Goods (FMCG) and returns distribution network. This study focused on analyzing the current performance of the distribution network and evaluating the scenarios for K+N&rsquo / s future distribution network by a simulation study.

In this thesis, we study the development of linguistic concepts (corresponding to a subset of nouns, verbs and adjectives) on a humanoid robot. To accomplish this goal, we use affordances, a notion first proposed by J.J. Gibson to describe the action possibilities offered to an agent by the environment. Using the affordances formalization framework of Sahin et al., we have implemented a learning system on a humanoid robot and obtained the required data from the sensorimotor experiences of the robot. The system we developed (1) can learn verb, adjective and noun concepts, (2) represent them in terms of strings of prototypes and dependencies based on affordances, (3) can accurately recognize the concept of novel objects and events, and (4) can be used for tasks such as goal emulation and multi step planning.

This study attempts to integrate geophysical data with other spatial data using Geographic Information Systems (GIS). The study is carried out in a part of Galatean Volcanic Province, north of Ankara. Gravity, magnetic, topographic, rock type and volcanic eruption center data are the data layers used in the study. All data layers are converted to raster format with a grid spacing of 100 m. The first step in the analysis is the pair-wise analyses of all data layers. For the geophysical data, different layers for the depths of 1 to 5 km are generated. All paired analyses indicate that geophysical and other data sets are correlative among each other but show no relationship for any two layers from different sets. In the second step of the analyses, two geophysical data are combined and overlaid with layers from the other set. These analyses indicate that relationship between geophysical data with other spatial data becomes more evident and that the geophysical data can be successfully integrated with other data sets. Accuracy of the results is highly dependent on the accuracy of both data sets. Analyses have shown GIS can contribute to the investigation of spatial distribution of buried planar structures using geophysical data.

In this thesis, information systems success measurement practices and expectations for information technology investments of four companies in Turkey are examined. The aim of this study is to understand the information systems success measurement practices of the studied companies and the relation between the expectations for IT investment and IS success of these companies in Turkey.

This study is concerned with a sorting problem / the placement of alternatives into preference classes in the existence of multiple criteria. An interactive model is developed to address the problem, assuming that the decision maker has an underlying utility function which is linear. A recent methodology, Even-Swaps, which is based on value tradeoff is utilized in the model for both making an estimation of the underlying utility function and generating possible dominance among the alternatives on which it is performed. Convex combinations, dominance relations, weight space reduction, Even-Swaps and direct decision maker placements are utilized to place alternatives in preference classes. The proposed algorithm is experimented with randomly generated alternative sets having different characteristics.

This study presents a new shape representation scheme based on disconnected symmetry axes along with a matching framework to address the problem of generic shape recognition. The main idea is to define the relative spatial arrangement of local symmetry axes in a shape centered coordinate frame. The resulting descriptions are invariant to scale, rotation, small changes in viewpoint and articulations. Symmetry points are extracted from a surface whose level curves roughly mimic the motion by curvature. By increasing the amount of smoothing on the evolving curve, only those symmetry axes that correspond to the most prominent parts of a shape are extracted. The representation does not suffer from the common instability problems of the traditional connected skeletons. It captures the perceptual properties of shapes well. Therefore, finding the similarities and the differences among shapes becomes easier. The matching process is able to find the correct correspondence of parts under various visual transformations. Highly successful classification results are obtained on a moderate sized 2D shape database.

Makams are well-defined modes of classical Turkish music. They can be taken as the Turkish music counterparts of Western music tonal structures at a certain level. Nevertheless, makams have additional features such as the usage of specific notes resulting from their different architecture and the special use of scales (i.e. progression). The main goal of this study is to construct a platform for identifying makams through a computer program by proposing a machine learning mechanism. There are restrictionson the mechanism related to the characteristics of the task. Such a mechanism should represent real-time sequential input with continuous values, should handle possible errors in this input and show immediate learning with limited data. These restrictions are valid and necessary for an analogy with the act of listening to music. A Boltzmann machine, modified for this purpose is designed, implemented and used in this study as this learning mechanism. Two characteristics of this study define its significance. First, this study is on the structural features of makams of classical Turkish music. Second, the identifying mechanism is a Boltzmann machine having a different schema than statistical identification tasks in tonality induction.

It is a well-known fact that project activities are subject to considerable uncertainty, which may lead to multiple schedule disruptions during project execution. As a result, the random nature of activity durations has been the subject of numerous research efforts since the introduction of the initial PERT. A common problem which arises in project management is the fact that the planned schedule is often disrupted by several uncontrollable factors like weather conditions, other environmental factors, additional time that might be required for rework and correction of detected defects. As a result, project managers are often unable to meet the promised completion dates. It is therefore vital to take into account such possible disruptions and their potential negative consequences at the project schedule design stage. Hence, the ability of the pre-schedule to absorb disruptions may be very important in such settings. At this point two new criteria are used in modern scheduling literature: &quot / robustness&quot / and &quot / stability&quot / . In this thesis, we propose several stability measures. These measures are embedded in a tabu search algorithm to generate stable schedules in a multi resource environment subject to random disruptions.