Özlük, Özgür
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Name Variants
Özgür Özlük
Job Title
Email Address
ozluko@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
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Scholarly Output
24
Articles
3
Views / Downloads
1640/1063
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
17
Scopus Citation Count
21
Patents
0
Projects
1
WoS Citations per Publication
0.71
Scopus Citations per Publication
0.88
Open Access Source
21
Supervised Theses
0
| Journal | Count |
|---|---|
| Annals Of Operations Research | 1 |
| Journal of Public Procurement | 1 |
| Journal of the Operational Research Society | 1 |
| Proceedings of International Conference on Computers and Industrial Engineering, CIE | 1 |
Current Page: 1 / 1
Scopus Quartile Distribution
Competency Cloud
24 results
Scholarly Output Search Results
Now showing 1 - 10 of 24
Article Citation - WoS: 2Citation - Scopus: 5Increasing Procurement Efficiency Through Optimal E-Commerce Enablement Scheduling(Emerald Group Publishing Ltd., 2019-06-03) Özlük, Özgür; Cholette, Susan; Clark, Andrew GPurpose: This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings. Design/methodology/approach: This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints. Findings: The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions. Originality/value: The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period; additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.Master Term Project Employee Performance Prediction(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Sivas, Barış; Özgür ÖzlükDogGo is a company that aims to provide safe and professional dog walking and grooming services to dog owners through the mobile application. Thanks to the DogGo application, dog owners and people who is employee of company and wants to walk their dogs (to be called Walkers) can meet on the same platform on the mobile application interface. The problem was determined by company that they needed to be able to accurately predict the performance of the walkers in the upcoming dog-walker matches, thus ensuring the correct dog walker match. This study will be planned to serve to this company for calculating their current walkers’ performance in an accurate way. The relevant machine-learning model will first be based on the manual scoring system made by the company for the performance of existing employees, and then the model will be developed in the light of the gains obtained from this. For the performance of the model, the employees and their characteristics are important for the first time.Master Term Project Predicting the Reasonable Departments for the Human Resources Related Questions by Using the Text Classification Algorithms(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Sancı, Yavuz; Özlük, ÖzgürThe employees of Yapı Kredi Bank use a help desk system to ask their Human Resources related questions to the employees of the Human Resources departments. The questions are assigned automatically to the relevant departments by the system according to the subjects of the questions. In some cases, the mismatches between the contents and the subjects of the questions may cause the wrong Human Resources department assignments of the questions. Even though the application allows Human Resources employees to redirect the questions to the appropriate Human Resources departments, which are responsible for answering, the response time of these questions lasts longer. This project aims to analyze the content of the Human Resources related questions by using the text classification algorithms to predict the responsible Human Resources departments. Thus, it is aimed to respond to the questions in a much shorter time.Master Term Project Smart Precision Agriculture With Autonomous Irrigation System Using Rnn-Based Techniques(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Anuşlu, Timuçin; Özlük, ÖzgürThe study presents a solution to improve freshwater usage for irrigation in the agriculture by building a neural network model to predict soil moisture at 20 cm level with time series data over longer periods of time.Master Term Project Tractor Sales Forecast Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, ÖzgürThis study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.Master Term Project Predicting Facebook Ad Impressions & Cpm Values(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekten, Semih; Özlük, ÖzgürIt is estimated that there are more than two billion active users on Facebook as of the first quarter of 2018 and social media has tremendous opportunities for advertisers in terms of performance and measurability. However, for marketing managers, it is very difficult to manage all the campaigns on different marketing channels and optimize for better results.For that reason, Facebook Marketing Partners or other optimization solutions emerged in the adtech market. In order to improve existing optimization solutions in the market, ad impression costs will be predicted in this study by using different machine learning techniques and different algorithms. The main goal of this study is to generate a robust model for predicting CPM values on Facebook, and to use that model as an in put for the existing optimization solution Adphorus offers for its clients. Adphorus is one of the Facebook Marketing Partners in the market.Master Term Project Sentiment Analysis of Hürriyet Emlak(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Korkmaz, Alev; Özlük, ÖzgürSentiment analysis refer to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, whether the attitude behind a text is positive, negative or neutral.Master Term Project Steel Product Clustering and Feature-Based Product Price Estimation for Flat Secondary Materials(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kemerci, Meryem; Özlük, ÖzgürMachine Learning replaces manual and repeatable processes every day, none of the industries can resist these developments. Older systems were rule-based which would bring some level of automation, but all had their limits. One of the goals of Machine Learning is prediction, and it can be used to obtain higher accuracy and better forecasts. Price predictions are made by hand according to market expectations and countries’ conjuncture in the past, but it is changing fast with the developments of Artificial Intelligence tools. In steel Industry, price levels are determining based on human intuition and simpler statistics. Profits are directly connected to the right pricing for the right time, machine learning algorithms may do the quotation of the steel properly to increase the company profits. This study aims to classify items as per quality and estimate the price level for the products. Feature selection preprocessing steps are used to enhance the performance and scalability of the classification method. The second part is feature-based product price estimation for the secondary products and selects the predictors of each quality under the product family.Master Term Project Text Classification Using Apache Spark(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Azizoğlu, Umut Rezan; Özlük, ÖzgürOne of the biggest problems of enterprises which are marketplace e-commerce business model with social platform; The improper communication of their social platform is the negative impact of the customer experience and the damage of the brand's value both materially and morally. As the number of daily commentaries is in numbers that cannot be read manually with optimal human resources in terms of company profitability, the interpretation modules in social market places are left unconscious. With this Project; established a model that prevents sentences that spoil the customer experience in their social platforms. Both data preparation and machine learning model were developed on Databricks notebook, using the apache spark platform with SparkML libraries and Pyspark language. The “Text Classification” approach is adopted when determining the model.Conference Object Capacity Allocation and Pricing Policies for Cloud Computing Service Providers(Curran Associates Inc., 2018) Ünlüyurt T.; Özgür Özlük; Afghah, R.; Ünlüyurt, T.; Özlük, Ö.The cloud computing is regarded as a paradigm shift in today’s IT world. As cloud computing resources behave like perishable products, revenue management techniques can be applied to increase cloud service provider's total revenue. In this paper, we propose various methods for pricing and capacity allocation. We consider three types of instances offered by the service provider; subscription, on-demand and spot instances. We introduce three allocation and pricing policies and propose different models. We simulate these models on a randomly generated dataset and evaluate the models for different capacities. The results we obtain indicate the sensitivity of revenue to varying policies and demonstrate the potential profit increase for cloud service providers. © 2018, Curran Associates Inc. All rights reserved.
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