Küçükaydın, Hande

Profile URL
https://hdl.handle.net/20.500.11779/2665
Name Variants
Küçükaydın, H. & Hande Küçükaydın & Küçükaydın, Hande
Job Title
Email Address
kucukaydinh@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
Website
ORCID ID
0000-0003-2527-2064
Scopus Author ID
36655949100
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
DXX-7824-2022
Files
Hande Küçükaydın.jpg (72.44 KB)

Research Topics

Domains
Physical SciencesSocial Sciences
Fields
EngineeringBusiness, Management and Accounting
Subfields
Industrial and Manufacturing EngineeringAutomotive EngineeringOrganizational Behavior and Human Resource ManagementBuilding and Construction
Specific Research Areas
Vehicle Routing Optimization Methods
Transportation and Mobility Innovations
Facility Location and Emergency Management
Advanced Manufacturing and Logistics Optimization
Urban and Freight Transport Logistics

Sustainable Development Goals

NO POVERTY1
NO POVERTY
0
Research Products
ZERO HUNGER2
ZERO HUNGER
0
Research Products
GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
Research Products
QUALITY EDUCATION4
QUALITY EDUCATION
0
Research Products
GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
1
Research Products
REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
Research Products
SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
CLIMATE ACTION13
CLIMATE ACTION
0
Research Products
LIFE BELOW WATER14
LIFE BELOW WATER
0
Research Products
LIFE ON LAND15
LIFE ON LAND
0
Research Products
PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents

7

Citations

57

h-index

5

Go to Scopus profile
Documents

11

Citations

202

Go to WoS profile

Publication Collaboration

Affiliation Name Count
University of Liège 18
MEF University 13
Institut Superieur de Gestion 13
Boğaziçi University 6
Bahçeşehir University 1
1 / 2
Data obtained from OpenAlex
Scholarly Output

22

Articles

6

Views / Downloads

1334/850

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

43

Scopus Citation Count

46

Patents

0

Projects

3

WoS Citations per Publication

1.95

Scopus Citations per Publication

2.09

Open Access Source

18

Supervised Theses

0

JournalCount
2015 World Conference on Technology, Innovation and Entrepreneurship1
29th Meeting of Belgian Operational Research Society,Louvain-La-Neuve1
Computers & Industrial Engineering1
Euro1
International Transactions in Operational Research1
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2015 - 2019172020 - 20265

Settings

Author of Publication: search.filters.isAuthorOfPublication.dd669147-971f-4d2a-af0a-4e0e8aa9bd94

Scholarly Output Search Results

Now showing 1 - 10 of 22
  • Master Term Project
    Fastseller&worstseller Project (boston Matrix Text Classification Analysis)
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunçel, Ahmet; Küçükaydın, Hande
    …
  • Master Term Project
    Price Prediction Using Machine Learning Techniques: an Application To Vacation Rental Properties
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Ay, Oğuz; Hande Küçükaydın
    Pricing is a subjective process that highly depends on person. There is no general rule to price a house. That is why there is both overpriced and underpriced rental houses in rental listings in websites such as AirBnB. In order to reduce the effect of subjective pricing, a general machine learning model is built in this project to make more objective price predictions.In the literature, there are different machine learning models to make numeric predictions. Physical features of houses are used as an input to make inferences about the price of a house. These machine learning models can identify the relations between features and the price and make the predictions with respect to features of a new listing house that has not been priced before.In this project, six different machine learning models are developed. These are linear regression, ridge regression, support vector regressor, random forest regressor, light gradient boosting machine regressor and extreme gradient boosting regressor. The performances of all models are compared, and the best model is selected for hyper-parameter tuning to make more accurate predictions.
  • Master Term Project
    Association Rule Mining on Ticket Sales Data
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Genç, Özge; Küçükaydın, Hande
    This study aims to analyze the ticket sales data of a cultural institution and define association rules between the festivals/event group and festival/event group venues by market basket analysis. Market basket analysis is a well-known data mining method that is used to discover similarities between products or product groups. For market basket analysis, the apriori algorithm is applied which is considered as a popular data mining algorithm and helps to discover frequent item sets and forms association rules within the dataset. In this project, the apriori algorithm is applied using Python to discover the association rule: between the venues (implementation 1), between the venues excluding the venues used for a specific festival type (implementation 2), between festivals and event groups if any (implementation 3). According to the results of implementation 1, the associations are mostly between the venues of a specific festival type. According to the implementation 2, when we exclude this specific festival type from the dataset, it is seen the rules are mostly between the venues of another festival type. In implementation 3, when the festival venues variable is excluded and only the event names are considered, it is seen that the support, lift and confidence values are lower than the previous implementations.
  • Master Term Project
    Suicide Tendency Classification and Suicide Number Prediction Forpopulation Subgroups
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ak, Mehmet; Küçükaydın, Hande
    Suicide is becoming a bigger problem for the world day by day and detecting population subgroups who are more prone to suicide is seen as one of the most important steps for taking precautions to decrease the suicide rates. This study consists of five machine learning models for suicide tendency classification and three machine learning models for prediction of suicide numbers by population subgroups. The dataset provided by World Health Organization is used in the project. Obtained models classify population subgroups as suicide-prone or less suicide prone with 86% accuracy and explain 90 % of the variance in the suicide number per 100,000 population of specific countries.
  • Article
    A Comparative Study of Branch-And Algorithms for Vehicle Routing With Time Windows and Waiting Time Costs
    (Wiley, 2026-02-09) Michelini, Stefano; Kucukaydin, Hande; Arda, Yasemin
    Branch-and-price is one of the most commonly used methodologies for solving routing problems. In recent years, several studies have investigated advanced labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Such algorithms include efficient techniques such as decremental state space relaxation, ng-route relaxation, and several hybridizations of these two relaxation methods. In this study, we compare the performance of these labeling algorithms in a branch-and-price framework when applied to the vehicle routing problem with time windows and a variant of this problem in which waiting times have a linear cost. For the latter problem, we also propose an appropriate label structure with associated resource extension functions and dominance rules. We perform these comparisons by using a rigorous methodology, which consists of parameterizing several features of these algorithms, obtaining a good parameter configuration for each algorithm, and analyzing the performance of these configurations on benchmark instances. In order to obtain good configurations, we make use of irace, which is a tool for automated parameter tuning, while statistical tests are used for performance comparisons. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others.
  • Conference Object
    Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times
    (2016) Michelini, S; Arda, Y; Küçükaydın, Hande
    ...
  • Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Optimal Keyword Bidding in Search-Based Advertising With Budget Constraint and Stochastic Ad Position
    (Taylor & Francis, 2019-04-20) Özlük, Özgür; Selçuk, Barış; Küçükaydın, Hande
    This paper analyses the search-based advertising problem from an advertiser’s view point, and proposes optimal bid prices for a set of keywords targeted for the advertising campaign. The advertiser aims to maximise its expected potential revenue given a total budget constraint from a search-based advertising campaign. Optimal bid prices are formulated by considering various characteristics of the keywords such that the expected revenue from a keyword is a function of the ad’s position on the search page, and the ad position is a stochastic function of both the bid price and the competitive landscape for that keyword. We explore this problem analytically and numerically in an effort to generate important managerial insights for campaign setters.
  • Conference Object
    Combining Acceleration Techniques for Pricing in a Vrp With Time Windows
    (2016) Michelini, S; Arda, Y; Küçükaydın, Hande
    ...
  • Master Term Project
    Forecasting With Ensemble Methods: an Application Using Fashion Retail Sales Data
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yüzbaşıoğlu, Orkun Berk; Küçükaydın, Hande
    In this project, ensemble methods of machine learning are used to predict short term store sales of a fashion retailer. Sales forecasts of various products at different stores are generated for a span of three months with bagging tree regressor, random forest regressor, and gradient boosting regressor algorithm. Algorithms are trained and evaluated with real past sales data of a Turkish fashion retailer. The predictive performance of the models is compared with linear regression. The results of the study show that random forest regressor shows the best performance
  • Master Term Project
    Hotel Recommendation for Online Travel Agencis
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kılıçlı, Cem; Küçükaydın, Hande
    Since the early 2000s, online travel agencies (OTAs) have become a central online market source, used by millions of users in all over the world. Recommendation systems became one of the essential tools for them to increase their profit.