Taş, Duygu

Profile URL
https://hdl.handle.net/20.500.11779/2669
Name Variants
Duygu Taş Küten
Job Title
Email Address
tasd@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Former Staff
Website
ORCID ID
0000-0002-3579-4600
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
HPG-9160-2023

Research Topics

Domains
Physical SciencesSocial Sciences
Fields
EngineeringBusiness, Management and AccountingSocial Sciences
Subfields
Industrial and Manufacturing EngineeringAutomotive EngineeringManagement Information SystemsTransportation
Specific Research Areas
Vehicle Routing Optimization Methods
Transportation and Mobility Innovations
Supply Chain and Inventory Management
Advanced Manufacturing and Logistics Optimization
Transportation Planning and Optimization

Sustainable Development Goals

SDG data is not available
This researcher does not have a Scopus ID.
Documents

13

Citations

710

Go to WoS profile

Publication Collaboration

Affiliation Name Count
Sabancı Üniversitesi 8
Eindhoven University of Technology 7
HEC Montréal 4
MEF University 4
Ankara University 3
1 / 4
Data obtained from OpenAlex
Scholarly Output

8

Articles

4

Views / Downloads

434/168

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

119

Scopus Citation Count

122

Patents

0

Projects

0

WoS Citations per Publication

14.88

Scopus Citations per Publication

15.25

Open Access Source

3

Supervised Theses

0

JournalCount
28th European Conference on Operational Research1
Avrupa Bilim ve Teknoloji Dergisi = European Journal of Science and Technology (EJOSAT)1
European Journal Of Operational Research1
INFORMS Annual Meeting1
International Journal of Production Research1
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2015 - 201952020 - 20213

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Author of Publication: search.filters.isAuthorOfPublication.04cc8740-944a-4127-8c52-bbbe92f03056

Scholarly Output Search Results

Now showing 1 - 8 of 8
  • Master Term Project
    Credit Card Froud Detection Using Machine Learning
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Erdoğan, Tibet; Duygu Taş Küten
    This project aims to find the most efficient machine learning models to detect fraudulent transactions on credit cards. The dataset used for this project consists of credit card transactions made by European cardholders in September 2013. This dataset presents transactions that have occurred in two days, where there are 492 frauds out of 284,807 transactions. Machine learning methods, such as decision trees, logistic regression and random forest classifier are used to predict the fraudulent transactions. Performance of these machine learning models are compared to achieve the highest accuracy. According to the results, it is found that the random forest classifier is the most effective model, and the SMOTE technique used to overcome the data imbalance performs better than the under-sampling technique. It is also observed that the models employed with the under-sampled data misclassify large number of non-fraud transactions as fraud. Lastly, by means of the random forest with the over-sampling technique (SMOTE), it is observed that the feature “V13” has the most important role in detecting fraud.
  • Conference Object
    The Traveling Salesman Problem With Time-Dependent Service Times
    (2016) Taş, Duygu
    This paper introduces a version of the classical traveling salesman problem with time-dependent service times. In our setting, the duration required to provide service to any customer is not fixed but defined as a function of the time at which service starts at that location. The objective is to minimize the total route duration, which consists of the total travel time plus the total service time. The proposed model can handle several types of service time functions, e.g., linear and quadratic functions. We describe basic properties for certain classes of service time functions, followed by the computation of valid lower and upper bounds. We apply several classes of subtour elimination constraints and measure their effect on the performance of our model. Numerical results obtained by implementing different linear and quadratic service time functions on several test instances are presented.
  • Article
    Citation - WoS: 44
    Citation - Scopus: 42
    Electric Vehicle Routing With Flexible Time Windows: a Column Generation Solution Approach
    (Taylor & Francis, 2020-01-10) Taş, Duygu
    In this paper, we introduce the Electric Vehicle Routing Problem with Flexible Time Windows (EVRPFTW) in which vehicles are allowed to serve customers before and after the earliest and latest time window bounds, respectively. The objective of this problem is to assign electric vehicles to feasible routes and make schedules with minimum total cost that includes the traveling costs, the costs of using electric vehicles and the penalty costs incurred for earliness and lateness. The proposed mathematical model is solved by a column generation procedure. To generate an integer solution, we solve an integer programming problem using the routes constructed by the column generation algorithm. We further develop a linear programming model to compute the optimal times to start service at each customer for the selected routes. A number of wellknown benchmark instances is solved by our solution procedure to evaluate the operational gains obtained by employing flexible time windows.
  • Article
    Citation - WoS: 55
    Citation - Scopus: 57
    Branch-And Methods for the Electric Vehicle Routing Problem With Time Windows
    (Taylor and Francis, 2021-07-31) Çatay, Bülent; Duman, Ece Naz; Taş, Duygu
    In this paper, we address the electric vehicle routing problem with time windows and propose two branch-and-price-and-cut methods based on a column generation algorithm. One is an exact algorithm whereas the other is a heuristic method. The pricing sub-problem of the column generation method is solved using a label correcting algorithm. The algorithms are strengthened with the state-of-the-art acceleration techniques and a set of valid inequalities. The acceleration techniques include: (i) an intermediate column pool to prevent solving the pricing sub-problem at each iteration, (ii) a label correcting method employing the ng-route algorithm adopted to our problem, (iii) a bidirectional search mechanism in which both forward and backward labels are created, (iv) a procedure for dynamically eliminating arcs that connect customers to remote stations from the network during the path generation, (v) a bounding procedure providing early elimination of sub-optimal routes, and (vi) an integer programming model that generates upper bounds. Numerical experiments are conducted using a benchmark data set to compare the performances of the algorithms. The results favour the heuristic algorithm in terms of both the computational time and the number of instances solved. Moreover, the heuristic algorithm is shown to be specifically effective for larger instances. Both algorithms introduce a number of new solutions to the literature.
  • Conference Object
    A Capacitated Lot Sizing Problem With Stochastic Setup Times
    (2015) Taş, Duygu
    In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times (CLSP-SST).
  • Article
    Citation - WoS: 20
    Citation - Scopus: 23
    A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
    (Elsevier Science bv, 2019-02-01) Jabali, Ola; Gendreau, Michel; Jans, Raf; Taş, Duygu
    In this paper, we study a Capacitated Lot Sizing Problem with Stochastic Setup Times and Overtime (CLSPSSTO). We describe a mathematical model that considers both regular costs (including production, setup and inventory holding costs) and expected overtime costs (related to the excess usage of capacity). The CLSP-SSTO is formulated as a two-stage stochastic programming problem. A procedure is proposed to exactly compute the expected overtime for a given setup and production plan when the setup times follow a Gamma distribution. A sample average approximation procedure is applied to obtain upper bounds and a statistical lower bound. This is then used to benchmark the performance of two additional heuristics. A first heuristic is based on changing the capacity in the deterministic counterpart, while the second heuristic artificially modifies the setup time. We conduct our computational experiments on well-known problem instances and provide comprehensive analyses to evaluate the performance of each heuristic. (C) 2018 Elsevier B.V. All rights reserved.
  • Article
    Stokastik Süreler İçeren Kapasite Kısıtlı Parti Büyüklüğü Belirleme Problemi
    (EJOSAT - DergiPark, 2019-08-31) Taş, Duygu
    Bu makalede üretim ve kurulum süreleri stokastik olan kapasite kısıtlı çok ürünlü dinamik parti büyüklüğü belirleme problemi ele alınmıştır. Bu problemde tüm sürelerin stokastik olduğu durum göz önünde bulundurularak hem verimli hem de güvenilir üretim planları elde edilmektedir. Ele alınan problemin amacı klasik üretim maliyetleri ve ek mesai maliyetlerinden oluşan toplam maliyeti en küçüklemektir. Klasik maliyetler, üretim, kurulum ve envanter tutmaktan kaynaklanmaktadır. Ek mesai maliyetleri ise makinenin zaman kapasitesini aşacak şekilde kullanılmasından dolayı ortaya çıkmaktadır. Öncelikle, belirli bir üretim ve kurulum planı için beklenen ek mesai süresini kesin olarak hesaplayan bir prosedür önerilmiştir. Problemi etkin bir şekilde çözmek için tabu algoritmasına dayanan bir çözüm yaklaşımı geliştirilmiştir. Bu yaklaşım üç aşamadan oluşmaktadır: Başlangıç, iyileştirme ve planlama. Algoritmanın ilk aşamasında olurlu planlar üreten bir başlangıç metodu önerilmiştir. Bulunan planlar makalede önerilen tabu arama metoduyla iyileştirilmektedir. Planlama aşamasında, yerel arama metodunun bulduğu çözümleri iyileştirmek için bir doğrusal programlama modeli geliştirilmiştir. Çözüm yöntemimizin performansı literatürde yayınlanmış alt sınırlar kullanılarak onaylanmıştır. Ayrıca, sonuçlar tabu arama yöntemimizin makul sürelerde çok iyi çözümler elde ederek iyi performans sergilediğini göstermektedir.
  • Conference Object