Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Plays Well With Others: A Study of EFL Learner Reciprocity in Action

Abstract: Classroom observation studies suggest that the teacher-calculus is complex and sensitive to learner behaviors. In language education, this becomes particularly important due to strong emphasis on productive interpersonal interaction. This study set out to understand what constitutes positive learning behaviors in an EFL context. The construct of learner reciprocity (Feuerstein, Feuerstein, Falik, & Rand, 2002; Lidz, 1991; Poehner, 2008; van der Aalsvoort & Lidz, 2002) was employed in analyzing classroom, interview, focus group, and research journal data amassed over a 2.5-month period. Two focal students, one more reciprocal case and one less reciprocal case, provided contrasting data sets that exemplify the presence and absence of learner reciprocity. Contrasting illustrations of learner reciprocity in action as a component of understanding language teaching and learning are presented and discussed.

Integrating configuration-based seismic design principles into architectural education: teaching strategies for lecture courses

Abstract: The importance of teaching seismic design in architecture schools is widely acknowledged and regarded as fundamental in earthquake-prone countries. Clear and consistent seismic design guidelines ar...

A genetic algorithm to solve the multidimensional knapsack problem

Abstract: In this paper, The Multidimensional Knapsack Problem (MKP) which occurs in many different applications is studied and a genetic algorithm to solve the MKP is proposed. Unlike the technique of the classical genetic algorithm, initial population is not randomly generated in the proposed algorithm, thus the solution space is scanned more efficiently. Moreover, the algorithm is written in C programming language and is tested on randomly generated instances. It is seen that the algorithm yields optimal solutions for all instances.

Container Demand Forecasting Using Machine Learning Methods: A Real Case Study from Turkey

Abstract: The container demands in ports significantly fluctuate over time and accurate container demand forecasting is essential for logistics companies because they can make their future business plans accordingly. In maritime transportation, container slot agreements are generally made two times in a year. A slot is one Twenty-Foot Equivalent Unit (TEU) space in a container ship and early booking of a slot is less costly for a company. Therefore, the accurate prediction of future container demands is crucial for companies to reduce their costs and increase their profits. In this study, we developed various forecasting models using machine learning methods to accurately predict the future container demands for the largest maritime transportation and logistics company of Turkey. The main aim is to provide accurate container demand forecasts for the company so that it can optimize the container slot bookings. To forecast the container demand, we used the company`s internal demand data as well as various external data such as gross domestic product (GDP), inflation rate, and exchange rate. We built four forecasting models based on Linear Regression, Boosted Decision Tree Regression, Decision Forest Regression and Artificial Neural Network Regression algorithms. The performances of these methods were evaluated according to Coefficient of Determination, Mean Absolute Error, Root Mean Square Error, Relative Absolute Error and Relative Squared Error. The case study showed that Boosted Decision Tree Regression and Decision Forest regression methods yield the best forecasting accuracy.

Türki̇ye mobi̇lya sektörüne yöneli̇k ölçek ve i̇li̇şki̇ anali̇zi̇

Abstract: The aim of this study is to develop a scale for the existing problems in the furniture industry and to analyze the relevance of the developed scale factors within the framework of supply chain management. In the study, literature research was done and the competition elements used in the furniture sector were determined. These identified elements were updated by using Delphi technique and a questionnare form was prepared. Factor analysis was performed with the data obtained in response to the responses given to the sector authorities. 16 elements were analyzed under three main factors; supply chain management, environment factor and innovative factor. Then, regression analysis was carried out to elaborate the relations among these three factors. 64% of supply chain management is explained by environmental elements and 23% by innovative elements. All of the hypotheses developed in the study were accepted.