This study presents a comprehensive benchmarking study of the top 100 U.S. Universities. The methodologies used to come up with insights into the domain are Data Envelopment Analysis (DEA) and information visualization. Various approaches to evaluating academic institutions have appeared in the literature, including a DEA literature dealing with the ranking of universities. Our study contributes to this literature by the extensive incorporation of information visualization and subsequently the discovery of new insights. The main purpose of the study is creating an objective basis of assessment for the candidate students to use for university preferences. Meanwhile, the actionable insights obtained for the domain can guide university managers, as well as candidate students.

Ertek G., Tokdil, B., Günaydın, İ., Göğüş, A. (2014) Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization, in I. Osman, A.L. Anouze and A. Emrouznejad (eds.) Strategic Performance Management and Measurement Using Data Envelopment Analysis, DOI: 10.4018/978-1-4666-4474-8, ISBN13: 9781466644748, IGI_Global (In Press).

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Competitiveness of Top 100 U.S. Universities: A Benchmark Study Using Data Envelopment Analysis (DEA) and Information Visualization

Dr. Gürdal Ertek recommends the following related books:

• Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software
• Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References 2006th Edition

 

 

 

 

This study applies the research of perception gap analysis to supply chain integration and develops a generic model, the 3-Level Gaps Model, with the goal of contributing to harmonization and integration in the supply chain. The model suggests that significant perception gaps may exist among supply chain members with regards to the importance of different performance criteria. The concept of the model is conceived through an empirical and inductive approach, combining the research discipline of supply chain relationship and perception gap analysis. First hand data has been collected through a survey across a key buyer in the motor insurance industry and its eight suppliers. Rigorous statistical analysis testified the research hypotheses, which in turn verified the validity and relevance of the developed 3-Level Gaps Model. The research reveals the significant existence of supply chain perception gaps at all three levels as defined, which could be the root-causes to underperformed supply chain.

Lu, D., Ertek, G., Betts, A. (2014) “Modelling the supply chain perception gaps”. The International Journal of Advanced Manufacturing Technology, 71(1-4), 731-751.

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Modelling the supply chain perception gaps

 

 

We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.

Ertek G., Sevinç, M., Ulus, F., Köse, Ö., Şahin, G. (2014) “Industrial Benchmarking through Information Visualization and Data Envelopment Analysis: A New Framework”, in I. Osman, A.L. Anouze and A. Emrouznejad (eds.) Strategic Performance Management and Measurement Using Data Envelopment Analysis, DOI: 10.4018/978-1-4666-4474-8, ISBN13: 9781466644748, IGI_Global (In Press).

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Industrial Benchmarking through Information Visualization and Data Envelopment Analysis: A New Framework

Dr. Gürdal Ertek recommends the following related books:

• Introduction to Data Envelopment Analysis and Its Uses: With DEA-Solver Software and References 2006th Edition
• Information Visualization: Design for Interaction (2nd Edition) 2nd Edition

 

There are more than 15 million college students in the US alone. Academic advising for courses and scholarships is typically performed by human advisors, bringing an immense managerial workload to faculty members, as well as other staff at universities. This paper reports and discusses the development of two educational expert systems at a private international university. The first expert system is a course advising system which recommends courses to undergraduate students. The second system suggests scholarships to undergraduate students based on their eligibility. While there have been reported systems for course advising, the literature does not seem to contain any references to expert systems for scholarship recommendation and eligibility checking. Therefore the scholarship recommender that we developed is first of its kind. Both systems have been implemented and tested using Oracle Policy Automation (OPA) software.

Engin, G., Aksoyer, B., Avdagic, M., Bozanlı, D., Hanay, U., Maden, M., Ertek, G. (2014) Rule-based expert systems for supporting university students. In Proceedings of 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. Procedia Computer Science 31 ( 2014 ) 22 – 31.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Rule-based expert systems for supporting university students

Dr. Gürdal Ertek recommends the following related books:

• Expert Systems: Principles and Programming, Fourth Edition 4th Edition
  
• Jess in Action: Java Rule-Based Systems (In Action series)

 

 

 

 

In this study, we adopt the classic capacitated p-median location model for the solution of a network design problem, in the domain of electric charge station network design, for a leading company in Turkey. Our model encompasses the location preferences of the company managers as preference scores incorporated into the objective function. Our model also incorporates the capacity concerns of the managers through constraints on maximum number of districts and maximum population that can be served from a location. The model optimally selects the new station locations and the visualization of model results provides additional insights.

Gavranović, H., Barut, A., Ertek, E., Yüzbaşıoğlu, O.B., Pekpostalcı, O., Tombuş, O., Optimizing the electric charge station network of EŞARJ. In Proceedings of 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. Procedia Computer Science 31 ( 2014 ) 15 – 21.

Note: This is the final draft version of this paper. Please cite this paper (or this final draft) as above.

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Optimizing the electric charge station network of EŞARJ

Dr. Gürdal Ertek recommends the following related books:

• Network Flows: Theory, Algorithms, and Applications 1st Edition
• Operations Research: An Introduction (9th Edition)
• Operations Research: Applications and Algorithms 3rd Revised & enlarged Edition
• Designing and Managing the Supply Chain 3e with Student CD 3rd Edition

 

 

The topic of this paper is the Alzheimer’s Disease (AD), with the goal being the analysis of risk factors and identifying tests that can help diagnose AD. While there exists multiple studies that analyze the factors that can help diagnose or predict AD, this is the first study that considers only non-image data, while using a multitude of techniques from machine learning and data mining. The applied methods include classification tree analysis, cluster analysis, data visualization, and classification analysis. All the analysis, except classification analysis, resulted in insights that eventually lead to the construction of a risk table for AD. The study contributes to the literature not only with new insights, but also by demonstrating a framework for analysis of such data. The insights obtained in this study can be used by individuals and health professionals to assess possible risks, and take preventive measures.

Ertek, G., Tokdil, B., Günaydın, İ. “Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis”. In Proceedings of Industrial Conference on Data Mining (ICDM 2014), Springer. Ed: Petra Perner (2014)

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Risk Factors and Identifiers for Alzheimer’s Disease: A Data Mining Analysis

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Dr. Gürdal Ertek recommends the following related books:

• Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition
• Data Mining and Analysis: Fundamental Concepts and Algorithms

 

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Preventive health care is unlike health care for acute ailments, as people are less alert to their unknown medical problems. In order to motivate public and to attain desired participation levels for preventive programs, the attractiveness of the health care facility is a major concern. Health economics literature indicates that attractiveness to a facility is significantly influenced by proximity of the clients to it. Hence attractiveness is generally modelled as a function of distance. However, abundant empirical evidence suggests that other qualitative factors such as perceived quality, attractions nearby, amenities, etc. also influence attractiveness. Therefore, a realistic measure should incorporate the vagueness in the concept of attractiveness to the model. The public policy makers should also maintain the equity among various neighborhoods, which should be considered as a second objective. Finally, even though general tendency in the literature is to focus on health benefits, the cost effectiveness is still a factor that should be considered. In this paper, a fuzzy bi-objective model with budget constraints of the problem is developed. Later, by modelling the attractiveness by means of fuzzy triangular numbers and treating the budget constraint as a soft constraint, a modified (and more realistic) version of the model is introduced. Two solution methodologies, namely fuzzy goal programming and fuzzy chance constrained optimization are proposed as solutions. Both the original and the modified models are solved within the framework of a case study in Istanbul, Turkey. In the case study, the Microsoft Bing Map is utilized in order to determine more accurate distance measures among the nodes.

Please cite the paper as follows:

Davari, S., Kilic, K., & Ertek, G. (2015). Fuzzy bi-objective preventive health care network design. Health Care Management Science, 18(3), 303-317. https://doi.org/10.1007/s10729-014- 9293-z

Dr. Gürdal Ertek recommends the following related books:

Network Flows: Theory, Algorithms, and Applications 1st Edition

Essentials of Business Analytics 2nd Edition

Information Visualization: An Introduction, 3rd Ed. 2014 Edition