Kaya, Rukiye

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12573/2685
Name Variants
Kaya, Rukiye
Job Title
Dr. Öğr. Üyesi
Email Address
rukiye.kaya@agu.edu.tr
Main Affiliation
02.02. Endüstri Mühendisliği
Status
Current Staff
Website
ORCID ID
0009-0003-5881-0305
Scopus Author ID
57209215893
Turkish CoHE Profile ID
388548
Google Scholar ID
5ft8-CIAAAAJ
WoS Researcher ID
HTQ-4535-2023

Sustainable Development Goals

SDG data is not available
Documents

5

Citations

126

h-index

3

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Documents

4

Citations

103

Go to WoS profile
Scholarly Output

4

Articles

2

Views / Downloads

5/3

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

19

Scopus Citation Count

20

WoS h-index

1

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

4.75

Scopus Citations per Publication

5.00

Open Access Source

3

Supervised Theses

0

JournalCount
Annals of Operations Research2
-- 8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- Malatya; Inonu University, Faculty of Engineering -- 2034231
Bitlis Eren Üniversitesi Fen Bilimleri Dergisi1
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Scholarly Output Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Improving Salary Offer Processes With Classification Based Machine Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kaya, Rukiye; Saatci, Mehtap; Bakal, Gokhan
    In job applications, salary is major motivational factor for employees and making accurate salary prediction is crucial for both employers and employees. Utilizing advanced technologies can significantly enhance the accuracy and efficiency of salary prediction process. In this study, we explore Machine Learning (ML) methods to enhance salary prediction process. We evaluated seven classification models for predicting salary categories, with the Artificial Neural Network (ANN) model achieving the highest accuracy at 58.2% on the test dataset, followed by the K-Nearest Neighbors (KNN) model with an accuracy of 56.8%. Additionally, we employed ensemble models to further enhance prediction accuracy. Among these, the Majority Voting Classifier using Hard Voting achieved the highest accuracy at 59.3%, demonstrating the potential of ensemble techniques in refining salary predictions. The developed salary prediction tool estimates the most appropriate salary category for each candidate and help mitigate potential biases in manual salary assessments, hence enables a more objective and consistent compensation system. ∗CRITICAL: Do Not Use Symbols, Special Characters, or Math in Paper Title or Abstract, and do not cite other papers in the abstract. © 2024 Elsevier B.V., All rights reserved.
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    Correction
    A Novel Integration of MCDM Methods and Bayesian Networks: The Case of Incomplete Expert Knowledge
    (Springer, 2024) Kaya, Rukiye; Salhi, Said; Spiegler, Virginia
  • Thumbnail Image
    Article
    Citation - WoS: 19
    Citation - Scopus: 19
    A Novel Integration of Mcdm Methods and Bayesian Networks: The Case of Incomplete Expert Knowledge
    (Springer, 2023) Kaya, Rukiye; Salhi, Said; Spiegler, Virginia
    In this study, we propose an effective integration of multi criteria decision making methods and Bayesian networks (BN) that incorporates expert knowledge. The novelty of this approach is that it provides decision support in case the experts have partial knowledge. We use decision-making trial and evaluation laboratory (DEMATEL) to elicit the causal graph of the BN based on the causal knowledge of the experts. BN provides the evaluation of alternatives based on the decision criteria which make up the initial decision matrix of the technique for order of preference by similarity to the ideal solution (TOPSIS). We then parameterize BN using Ranked Nodes which allows the experts to submit their knowledge with linguistic expressions. We propose the analytical hierarchy process to determine the weights of the decision criteria and TOPSIS to rank the alternatives. A supplier selection case study is conducted to illustrate the effectiveness of the proposed approach. Two evaluation measures, namely, the number of mismatches and the distance due to the mismatch are developed to assess the performance of the proposed approach. A scenario analysis with 5% to 20% of missing values with an increment of 5% is conducted to demonstrate that our approach remains robust as the level of missing values increases.
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    Article
    Optimization of Warehouse Location and Inventory Management for an Industrial Textile Manufacturer Company in Türkiye
    (2024) Kaya, Rukiye; Tutkun, Tutku; Nergiz, İrem Nur; Satic, Ugur
    In this study, we consider the demand forecasting, facility location, and inventory management problems of an industrial textile manufacturer company in Türkiye. First, we begin with the demand forecasting problem for thirty-two different products and employ ABC analysis to categorise the products. Then we test multiple forecasting methods and find out that Exponential Smoothing and Croston's TSB methods perform better in our categories. Using the demand forecast results in the facility location problem, we search for a location in Europe for a warehouse. For the facility location problem, we use a mixed-integer nonlinear mathematical model to minimise the transportation cost, and warehouse rental cost. We solve the model by using GAMS Solver. Then, we handle the inventory management problem and determine the quantity of the products that are sent from the factory and the warehouse to the customer. We propose a genetic algorithm approach that generates reorder quantities and reorder points for both the factory and the warehouse to minimise the total logistics costs, including holding, ordering and stockout costs. We use simulation models to calculate the logistics costs then we use these costs as fitness values to choose the best reorder quantities and reorder points. The proposed approach offers improvement in demand forecasting, inventory management, and facility location problems and brings up a 26% reduction in total logistic costs.