H. Kemal Ilter, BEng, MBA, PhD
Senior Member, ACM

"Many of the views which have been advanced are highly speculative, and some no doubt will prove erroneous, but I have in every case given the reasons which have led me to one view rather than another."

— Charles Darwin .

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Contents 1 Research Activities and Publications Laboratory Projects Private Research Website Tools 2 Teaching Choosing Paper Type Guidelines for a master's thesis proposal Notes on the PhD degree Monodisciplinary vs Pluridisciplinary Research Typical outline of a research paper What is a reaction paper? Anket Çalışmalarıyla Bilim Yapmak Bilimsel Çalışmalar Nasıl Okunur? 3 Bio Curriculum Vitae 4 Logbook Human Evolution The importance of stupidity in scientific research by M. A. Schwartz On the usefulness of useless knowledge by M. A. Schwartz Nature Beats Nurture by N. B. Tenker Programming Paradigms for Dummies by P. Van Roy Thoughts on the Time What is Life? by E. Schrödinger Bilgeliğe Giden Yol Mideden Geçer mi? Bilgi Üzerine Notlar Bilişim Toplumuna Evrilmek Üzerine Düşünceler

Tweets by hkilter

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Blogs

Oculus Supra A high level scientific viewpoint which includes weird, absurd, unusual but well-focused works/subjects from mad scientists' dedicated efforts.

Fitness Landscape A evolutionary perspective which is used to visualize the relationship between genotypes and reproductive success in evolutionary biology.

B'log A web blog about my daily activities, nothing more.

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Research Highlights

A multi-layer ontological model for supply chain systems In the supply chain research field, the question of which ontological structure is suitable for describing a particular supply chain system is still unclear due to the complex architecture of such systems. As a result, there are often problems with the successful implementation of the ontological models. This article presents a multi-layer ontological model that describes a supply chain. The model has four basic conceptual layers of knowledge; namely the Product, Agent, Flow, and Operation layers. Developers can add more layers or sub-layers depending on the complexity of a particular supply chain. Our model will provide a perspective to the area of supply chain management by splitting industry-specific issues into these familiar ontological layers. Using the ontological model developed, we also present a case study to illustrate a realistic application of the model. 180px Figure from Ilter, H. Kemal (2014): SCOM Layers. figshare. http://dx.doi.org/10.6084/m9.figshare.1040473 Retrieved 04:29, Sep 28, 2014 (GMT)

On the sub-optimality of strategic decisions Conceptual view of fitness and fitness measurement of strategic decisions on information systems, technological systems and innovation are becoming more important in recent years. This paper determines some dynamics of fitness landscape which are lead to termination of decision makers' search before reaching the global maximum in strategic decisions. These dynamics are specified according to management decision making models and supported with simulation results. This article determines simulation results by means of Fitness Value and Probability of Optimality. Correlation between these two concepts may be remarkable according to revealing optimal values in innovative and research-based decision making approaches beside sub-optimal results of traditional decision making approaches. A stochastic analysis of static complexity in manufacturing systems This paper presents static complexity in manufacturing systems. Static complexity can be viewed as a function of the structure of the system, connective patterns, variety of components and the strengths of interactions. We seek the acceptable static complexity levels for different types of manufacturing systems by means of their system performances. In general, deterministic processing times or expected values of processing times are considered to measure the complexity and related system performance in the production and operations management field. We consider stochastic processing requirements with a number of different processing distributions and levels of variability on the static complexity measurements. The objective of this paper is to reveal characteristics of the static complexity by making observations about the behavior of different systems in a hypothetical manufacturing environment. This would help in analyzing the properties in different manufacturing systems due to static complexity and system performance.

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References