Browsing by Subject "Decision-making--Mathematical models."

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Open Access
Application of AHP to multicriteria inventory classification
(1993) Güvenir, Nuray
In this thesis, a new method based on the application of Analytic Hierarchy Process (AHP) to ABC inventory classification is investigated. The traditional ABC classification method utilizes only the unit price and the annual usage of inventory items. However, in some cases, the classification done using only these two criteria turns out to be insufficient. The method described in this thesis enables the integration of several criteria that can be organized in a hierarchy into ABC classification. The method can be summarized as follows: A matrix is constructed by the pairwise comparison of criteria on the highest level. The elements of the eigen vector of this matrix represent the weights (priorities) of the criteria. If a criterion has subcriteria in the hierarchy, the weights computed in the similar manner for the subcriteria are multiplied by the weight of the criterion and inserted in its place. Repetition of these steps for aU levels of the hierarchy, the weight of all criteria are determined. Using the criteria weights determined by the AHP technique, the weighted score of each inventory item is computed. The items sorted by that weighted score are grouped in three classes: A, B, and C, as in the classical ABC classification. This new method is applied to the classification of inventory items used in rock excavation jobs done using blasting by a construction company. The same inventory is also classified according to the classical ABC technique, and the results are compared.
Open Access
Qualitative test-cost sensitive classification
(2008) Cebe, Mümin
Decision making is a procedure for selecting the best action among several alternatives. In many real-world problems, decision has to be taken under the circumstances in which one has to pay to acquire information. In this thesis, we propose a new framework for test-cost sensitive classification that considers the misclassification cost together with the cost of feature extraction, which arises from the effort of acquiring features. This proposed framework introduces two new concepts to test-cost sensitive learning for better modeling the real-world problems: qualitativeness and consistency. First, this framework introduces the incorporation of qualitative costs into the problem formulation. This incorporation becomes important for many real world problems, from finance to medical diagnosis, since the relation between the misclassification cost and the cost of feature extraction could be expressed only roughly and typically in terms of ordinal relations for these problems. For example, in cancer diagnosis, it could be expressed that the cost of misdiagnosis is larger than the cost of a medical test. However, in the test-cost sensitive classification literature, the misclassification cost and the cost of feature extraction are combined quantitatively to obtain a single loss/utility value, which requires expressing the relation between these costs as a precise quantitative number. Second, the proposed framework considers the consistency between the current information and the information after feature extraction to decide which features to extract. For example, it does not extract a new feature if it brings no new information but just confirms the current one; in other words, if the new feature is totally consistent with the current information. By doing so, the proposed framework could significantly decrease the cost of feature extraction, and hence, the overall cost without decreasing the classification accuracy. Such consistency behavior has not been considered in the previous test-cost sensitive literature. We conduct our experiments on three medical data sets and the results demonstrate that the proposed framework significantly decreases the feature extraction cost without decreasing the classification accuracy.
Open Access
Utilization of analytic hierarchy process in the long-range aircraft selection problem of Turkish Airlines
(1991) Kayalık, Hakan
Analytic Hierarchy Process (AHP) developed by T.L. Saaty has received widespread attention in the literature. It has been applied in various decision making cases. this study presents the AHP with its application to a recent multi-criteria decision problem of Turkish Airlines. Among three long-range commercial jet aircrafts, the best one for Turkish Airlines will be chosen using the AHP.