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Papers by Ali Osman Kusakci
Heritage and Sustainable Development
Customer Loyalty Programs are one of the handiest tools to raise brand awareness, and secure long... more Customer Loyalty Programs are one of the handiest tools to raise brand awareness, and secure long-term and strong ties between a brand and existing consumers. Airline companies have been using frequent flyer programs (FFPs) to retain customers with the expectation of increasing passengers’ loyalty levels. The purpose of this study was to examine the significance of FFPs for customer loyalty, which is of great help for customer retention in the civil aviation industry in the sample of passengers flying from the new Istanbul Airport. Furthermore, we questioned the effectiveness of various services and products offered within FFPs for loyalty, which is decomposed into two main components, behavioral, and attitudinal commitment of loyalty. We evaluated the significance of various demographic factors on passengers’ perception of FFPs services and privileges, and customer loyalty. The study confirmed the vital role of FFPs to build up brand loyalty, where profession, duration of the membe...
International Journal of Information Technology & Decision Making
This study proposes a three-stage holistic methodology combining an interval type-2 fuzzy analyti... more This study proposes a three-stage holistic methodology combining an interval type-2 fuzzy analytical hierarchy process (IT2F-AHP) and data envelopment analysis (DEA) to deal with the performance evaluation problems encountered in fuzzy decision environments. In the first stage, prospective inputs and outputs are determined by field studies. The second stage employs IT2F-AHP to identify the most appropriate performance indicators based on vague expert judgements. Finally, DEA is applied to the decision-making units (DMUs) based on the selected set of input and output measures. The proposed methodology proves its merit on a case study addressing the performance of real estate investment trusts (REITs) in Turkey during their 10-year journey of trading on Borsa Istanbul (BIST). The results demonstrate that the average scores for technical, pure technical and scale efficiencies are 66%, 80% and 80%, respectively. Considering the technical efficiency scores, Turkish REITs could have reduc...
Southeast Europe Journal of Soft Computing
Today, recycling of used products and materials has become an increasingly important sector. Mank... more Today, recycling of used products and materials has become an increasingly important sector. Mankind, who uses the natural resources unconsciously, has found ways to improve recycling techniques when they realized that resources are becoming increasingly depleted. In the automotive sector, which is one of the largest sectors in the world, natural resources are being used to a great extent. According to the statistics, in 2009, approximately 9 million end-of-life vehicles (in Europe were withdrawn from traffic. Undoubtedly, this figure shows the necessity and importance of designing reverse logistics network optimized for ELVs. This research aims to determine the gaps in the literature by examining the studies made from the past to the present day in the field of reverse logistic network design for vehicles completed their life cycle. In this article, the studies in the fieldare analyzed based on objective functions, decision variables, constr handling metod, optimization methods used. Considered work are clustered using a special artificial neural network tool Organizing Maps (SOM), and the frequencies of the characteristics are shown in the study. This study, which includes a review of the literature and a clustering of studies, aims to guidethe researchers wo design of rreverse logistics networks for ELVs.
Expert Systems with Applications
In broad terms, karyotyping is the process of examination and classification of human chromosome ... more In broad terms, karyotyping is the process of examination and classification of human chromosome images to diagnose genetic diseases and disorders. It requires time consuming manual examination of cell images by a cytogeneticist to distinguish chromosome classes from each other. Thus, a reliable autonomous human chromosome classification system not only saves time and money but also reduces errors due to the inadequate knowledge level of the expert. Human cell contains 23 pairs of chromosome, 22 autosomes and a pair of sex chromosomes. Hence, we face a multi-class classification task which represents a challenging case for any sort of classifier. In this work, to solve this classification problem, we propose a novel methodology consisting two stages: (i) data preparation and training, and (ii) testing. To determine the most informative content of the dataset several preliminary experiments are conducted and a Principal Component Analysis is done. Then, a single Support Vector Machine (SVM ij) is trained to separate a pair of classes, (i,j) where a numerical optimization method Pattern Search (PS), is employed to find the optimal parameters for the SVM ij. Considering 22 pairs of autosomes, 22 × 22 experts are trained and optimized. The cluster of experts, we obtain is named as Competitive SVM Teams (CSVMTs) where each SVM ij competes with the others to label a new classification instance. The final output of the classifier is determined by majority voteing. The results obtained on Copenhagen dataset proves the merit of the algorithm as correct classification rates (CRR) on train and test samples are 99.55% and 97.84% respectively, which are higher than any accuracy rate achieved so far in the related literature.
Global optimization is an essential part of any kind of system. Various algorithms have been prop... more Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COPs.
Computers & Operations Research, 2013
Although most of unconstrained optimization problems with moderate to high dimensions can be easi... more Although most of unconstrained optimization problems with moderate to high dimensions can be easily handled with Evolutionary Computation (EC) techniques, constraint optimization problems (COPs) with inequality and equality constraints are very hard to deal with. Despite the fact that only equality constraints can be used to eliminate a certain variable, both types of constraints implicitly enforce a relation between problem variables. Most conventional constraint handling methods in EC do not consider the correlations between problem variables imposed by the problem constraints. This paper relies on the idea that a proper genetic operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. With this in mind, we employ a (μ+λ)-Evolution Strategy with a simplified variant of Covariance Matrix Adaptation based mutation operator along an adaptive weight adjustment scheme. The proposed algorithm is tested on two test sets. The outperformance of the algorithm is significant on the first benchmark when compared with five conventional methods. The results on the second test set show that algorithm is highly competitive when benchmarked with three state-of-art algorithms. The main drawback of the algorithm is its slightly lower speed of convergence for problems with high dimension and/or large search domain.
Heritage and Sustainable Development
Customer Loyalty Programs are one of the handiest tools to raise brand awareness, and secure long... more Customer Loyalty Programs are one of the handiest tools to raise brand awareness, and secure long-term and strong ties between a brand and existing consumers. Airline companies have been using frequent flyer programs (FFPs) to retain customers with the expectation of increasing passengers’ loyalty levels. The purpose of this study was to examine the significance of FFPs for customer loyalty, which is of great help for customer retention in the civil aviation industry in the sample of passengers flying from the new Istanbul Airport. Furthermore, we questioned the effectiveness of various services and products offered within FFPs for loyalty, which is decomposed into two main components, behavioral, and attitudinal commitment of loyalty. We evaluated the significance of various demographic factors on passengers’ perception of FFPs services and privileges, and customer loyalty. The study confirmed the vital role of FFPs to build up brand loyalty, where profession, duration of the membe...
International Journal of Information Technology & Decision Making
This study proposes a three-stage holistic methodology combining an interval type-2 fuzzy analyti... more This study proposes a three-stage holistic methodology combining an interval type-2 fuzzy analytical hierarchy process (IT2F-AHP) and data envelopment analysis (DEA) to deal with the performance evaluation problems encountered in fuzzy decision environments. In the first stage, prospective inputs and outputs are determined by field studies. The second stage employs IT2F-AHP to identify the most appropriate performance indicators based on vague expert judgements. Finally, DEA is applied to the decision-making units (DMUs) based on the selected set of input and output measures. The proposed methodology proves its merit on a case study addressing the performance of real estate investment trusts (REITs) in Turkey during their 10-year journey of trading on Borsa Istanbul (BIST). The results demonstrate that the average scores for technical, pure technical and scale efficiencies are 66%, 80% and 80%, respectively. Considering the technical efficiency scores, Turkish REITs could have reduc...
Southeast Europe Journal of Soft Computing
Today, recycling of used products and materials has become an increasingly important sector. Mank... more Today, recycling of used products and materials has become an increasingly important sector. Mankind, who uses the natural resources unconsciously, has found ways to improve recycling techniques when they realized that resources are becoming increasingly depleted. In the automotive sector, which is one of the largest sectors in the world, natural resources are being used to a great extent. According to the statistics, in 2009, approximately 9 million end-of-life vehicles (in Europe were withdrawn from traffic. Undoubtedly, this figure shows the necessity and importance of designing reverse logistics network optimized for ELVs. This research aims to determine the gaps in the literature by examining the studies made from the past to the present day in the field of reverse logistic network design for vehicles completed their life cycle. In this article, the studies in the fieldare analyzed based on objective functions, decision variables, constr handling metod, optimization methods used. Considered work are clustered using a special artificial neural network tool Organizing Maps (SOM), and the frequencies of the characteristics are shown in the study. This study, which includes a review of the literature and a clustering of studies, aims to guidethe researchers wo design of rreverse logistics networks for ELVs.
Expert Systems with Applications
In broad terms, karyotyping is the process of examination and classification of human chromosome ... more In broad terms, karyotyping is the process of examination and classification of human chromosome images to diagnose genetic diseases and disorders. It requires time consuming manual examination of cell images by a cytogeneticist to distinguish chromosome classes from each other. Thus, a reliable autonomous human chromosome classification system not only saves time and money but also reduces errors due to the inadequate knowledge level of the expert. Human cell contains 23 pairs of chromosome, 22 autosomes and a pair of sex chromosomes. Hence, we face a multi-class classification task which represents a challenging case for any sort of classifier. In this work, to solve this classification problem, we propose a novel methodology consisting two stages: (i) data preparation and training, and (ii) testing. To determine the most informative content of the dataset several preliminary experiments are conducted and a Principal Component Analysis is done. Then, a single Support Vector Machine (SVM ij) is trained to separate a pair of classes, (i,j) where a numerical optimization method Pattern Search (PS), is employed to find the optimal parameters for the SVM ij. Considering 22 pairs of autosomes, 22 × 22 experts are trained and optimized. The cluster of experts, we obtain is named as Competitive SVM Teams (CSVMTs) where each SVM ij competes with the others to label a new classification instance. The final output of the classifier is determined by majority voteing. The results obtained on Copenhagen dataset proves the merit of the algorithm as correct classification rates (CRR) on train and test samples are 99.55% and 97.84% respectively, which are higher than any accuracy rate achieved so far in the related literature.
Global optimization is an essential part of any kind of system. Various algorithms have been prop... more Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COPs.
Computers & Operations Research, 2013
Although most of unconstrained optimization problems with moderate to high dimensions can be easi... more Although most of unconstrained optimization problems with moderate to high dimensions can be easily handled with Evolutionary Computation (EC) techniques, constraint optimization problems (COPs) with inequality and equality constraints are very hard to deal with. Despite the fact that only equality constraints can be used to eliminate a certain variable, both types of constraints implicitly enforce a relation between problem variables. Most conventional constraint handling methods in EC do not consider the correlations between problem variables imposed by the problem constraints. This paper relies on the idea that a proper genetic operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. With this in mind, we employ a (μ+λ)-Evolution Strategy with a simplified variant of Covariance Matrix Adaptation based mutation operator along an adaptive weight adjustment scheme. The proposed algorithm is tested on two test sets. The outperformance of the algorithm is significant on the first benchmark when compared with five conventional methods. The results on the second test set show that algorithm is highly competitive when benchmarked with three state-of-art algorithms. The main drawback of the algorithm is its slightly lower speed of convergence for problems with high dimension and/or large search domain.