Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942

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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).
A Lot-Sizing Problem in Deliberated and Controlled Co-Production Systems
(Taylor and Francis, 2021) Kabakulak, Banu; Ağralı, Semra; Taşkın, Z. Caner; Pamuk, Bahadır
We consider an uncapacitated lot sizing problem in co-production systems, in which it is possible to produce multiple items simultaneously in a single production run. Each product has a deterministic demand to be satisfied on time. The decision is to choose which items to co-produce and the amount of production throughout a predetermined planning horizon. We show that the lot sizing problem with co-production is strongly NP-Hard. Then, we develop various mixed-integer linear programming (MILP) formulation of the problem and show that LP relaxations of all MILPs are equal. We develop a separation algorithm based on a set of valid inequalities, lower bounds based on a dynamic lot-sizing relaxation of our problem and a constructive heuristic that is used to obtain an initial solution for the solver, which form the basis of our proposed Branch & Cut algorithm for the problem. We test our models and algorithms on different data sets and provide the results.
Büyük Ölçekli Etki Enbüyükleme Problemi için Lagrange Gevşetmesi Tabanlı Etkin Bir Çözüm Yöntemi
(AKÜ FEMÜBİD, 2020) Güney, Evren
Etki Enbüyükleme Problemi (EEP) büyük bir sosyal ağ içindeki en etkin K tane kişiyi seçen zor bir stokastik kombinatoryal eniyileme problemidir. Son yıllarda pek çok araştırmacının ilgisini çeken bu problem için çok sayıda etkin yöntem geliştirilmiştir. Sosyal ağdaki bilginin / etkinin yayılımı çeşitli ağ akış modelleri ile tasarlandığında, elde edilen problemin amaç fonksiyonunun alt-birimsel olduğu gözlemlenmiştir. Bu sebeple basit bir açgözlü algoritma ile (1-1/e) en kötü performans garantisine erişilmiştir. Ancak, aç gözlü algoritmanın büyük boyutlu problemlerde çok uzun çözüm süreleri gerektirmesi alternatif yöntem arayışlarına neden olmuştur. Son yıllarda geliştirilen yeni yöntemler genelde büyük boyutlu ağlarda kısa sürede iyi çözümler elde ederken (1-1/e) performans garantisini de korumaktadır. Ancak pek az sayıda çalışma problemin sadece en-iyi çözümüne odaklanmıştır. Bu çalışmada Lagrange gevşetmesi tabanlı ve EEP’yi eniyi / eniyiye yakın çözen ve ölçeklenebilen bir yöntem geliştirilmiştir. Bu çerçevede, öncelikle Örneklem Ortalama Yakınsaması ile özgün probleme yakınsayan belirgin bir matematiksel model kurulmuştur. Daha sonra bu model üzerinde düğüm tabanlı Lagrange gevşetmesi tekniği uygulanmıştır. İlgili yöntem bağımsız çağlayan ve doğrusal eşik bilgi yayılım modelleri varsayımı altında çeşitli boyutlardaki sosyal ağ veri setleri (Facebook, Enron, Gnutella, arXiv) üzerinde test edilmiştir. Bütün senaryolarda eniyi / eniyiye yakın çözümlere ulaşılırken yazındaki mevcut yöntemlere göre on kata kadar hızlanma sağlanmıştır.
Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times
(2016) Michelini, S; Arda, Y; Küçükaydın, Hande
...
Citation - WoS: 3
Citation - Scopus: 5
Consumer Loans' First Payment Default Detection: a Predictive Model
(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL, 2020) Sevgili, Türkan; Koç, Utku
A default loan (also called nonperforming loan) occurs when there is a failure to meet bank conditions and repayment cannot be made in accordance with the terms of the loan which has reached its maturity. In this study, we provide a predictive analysis of the consumer behavior concerning a loan’s first payment default (FPD) using a real dataset of consumer loans with approximately 600,000 records from a bank. We use logistic regression, naive Bayes, support vector machine, and random forest on oversampled and undersampled data to build eight different models to predict FPD loans. A two-class random forest using undersampling yielded more than 86% on all performance measures: accuracy, precision, recall, and F1-score. The corresponding scores are even as high as 96% for oversampling. However, when tested on the real and balanced dataset, the performance of oversampling deteriorates as generating synthetic data for an extremely imbalanced dataset harms the training procedure of the algorithms. The study also provides an understanding of the reasons for nonperforming loans and helps to manage credit risks more consciously.
Citation - Scopus: 1
Determining and Evaluating New Store Locations Using Remote Sensing and Machine Learning
(Tübitak, 2021) Ünsalan, Cem; Turgay, Zeynep Zerrin; Küçükaydın, Hande; Höke, Berkan
Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model, which calculates store revenues, shouldbe rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques, which provide a novel way for evaluating new store locations.
Determining the Most Vulnerable Components in a Transportatıon Network
(Yıldız Technical University, 2018) Küçükaydın, Hande; Aras, Necati
Transportation networks belong to the class of critical infrastructure networks since a small deterioration in the service provision has the potential to cause considerable negative consequences on everyday activities. Among the reasons for the deterioration we can mention the shutdown of a subway station, the closure of one or more lanes on a bridge, the operation of an airport at a much reduced capacity. In order to measure the vulnerability of transportation network, it is necessary to determine the maximum possible disruption by assuming that there is an intelligent attacker wishing to give damage to the components of the network including the stations/stops and linkages. Identifying the worst disruptions can be realized by using interdiction models that are formulated by a bilevel mathematical programming model involving two decision makers: leader and follower. In this paper, we develop such a model referred to as attacker-operator model, where the leader is a virtual attacker who wants to cause the maximum possible disruption in the transportation network by minimizing the amount of flow among the nodes of the network, while the follower is the system operator who tries to reorganize the flow in the most effective way by maximizing the flow after the disruption. The benefit of such a model to the system operator is to determine the most vulnerable stations and linkages in the transportation network on one hand, and to take precautions in preventing the negative effects of the disruption on the other hand.
Citation - WoS: 1
Citation - Scopus: 1
Evaluation of Learning Management Systems Using Interval Valued Intuitionistic Fuzzy-Z Numbers
(Anadolu Üniversitesi, 2023) Ucal Sarı, İrem; Sergi, Duygu
The use of online education tools has increased rapidly with the transition to distance education caused by the pandemic. The obligation to carry out all activities of face-to-face education online made it very important for the tools used in distance education to meet the increasing needs. In line with these needs, radical changes have occurred in the learning management systems used in distance education. Therefore, in this study, it is aimed to determine the features that the systems used in distance education should have and to compare the existing systems according to these features. For this purpose, a novel fuzzy extension, interval valued intuitionistic fuzzy Z-numbers, is defined for modeling uncertainty, and AHP and WASPAS methods using proposed fuzzy numbers are developed to determine the importance of decision criteria and compare alternatives.
Citation - WoS: 1
Facial Emotion Recognition Using Residual Neural Networks
(2024) Kırbız, Serap
Facial emotion recognition (FER) has been an emerging research topic in recent years. Recent automatic FER systems generally apply deep learning methods and focus on two important issues: lack of sufficient labeled training data and variations in images such as illumination, pose, or expression-related variations among different cultures. Although Convolutional Neural Networks (CNNs) are widely used in automatic FER, they cannot be used when the number of layers is large. Therefore, a residual technique is applied to CNNs and this architecture is named residual neural network. In this paper, an automatic facial emotion recognition method using residual networks with random data augmentation is proposed on a merged FER dataset consisting of 41,598 facial images of size 48 × 48 pixels from seven basic emotion classes. Experimental results show that ResNet34 with data augmentation performs better than CNN with a classification accuracy of 81%.
Citation - WoS: 6
Citation - Scopus: 11
Mixcycle: Unsupervised Speech Separation Via Cyclic Mixture Permutation Invariant Training
(IEEE, 2022) Karamatlı, Ertuğ; Kırbız, Serap
We introduce two unsupervised source separation methods, which involve self-supervised training from single-channel two-source speech mixtures. Our first method, mixture permutation invariant training (MixPIT), enables learning a neural network model which separates the underlying sources via a challenging proxy task without supervision from the reference sources. Our second method, cyclic mixture permutation invariant training (MixCycle), uses MixPIT as a building block in a cyclic fashion for continuous learning. MixCycle gradually converts the problem from separating mixtures of mixtures into separating single mixtures. We compare our methods to common supervised and unsupervised baselines: permutation invariant training with dynamic mixing (PIT-DM) and mixture invariant training (MixIT). We show that MixCycle outperforms MixIT and reaches a performance level very close to the supervised baseline (PIT-DM) while circumventing the over-separation issue of MixIT. Also, we propose a self-evaluation technique inspired by MixCycle that estimates model performance without utilizing any reference sources. We show that it yields results consistent with an evaluation on reference sources (LibriMix) and also with an informal listening test conducted on a real-life mixtures dataset (REAL-M).
Müşteri Hizmetleri Bölümünde Süreç Analizi ve Stratejik Planlama- Lastik Sektöründe Bir Uygulama
(Eskişehir Teknik Üniversitesi, 2020) Özuduruk, Semih Faruk; Sergi, Duygu; Sarı, İrem Ucal
Bu çalışma kapsamında, bir işletmenin süreç analizinin yapılması ve sonrasında işletme stratejisinin oluşturulması için gerekli analiz ve stratejik yönetim modelleri incelenmiştir. Daha sonra, işletme geneli için incelenen bu yöntemler, bir işletme özelinde müşteri hizmetleri bölümüne uygulanmıştır. Çalışma kapsamında, öncelikle SWOT analizi ile iş biriminin içinde bulunduğu mevcut durumun özellikleri belirlenmiş, sonrasında oluşturulan Genişletilmiş SWOT matrisi ile ortaya çıkan faktörlere uygun stratejiler belirlenmiştir. Stratejiler belirlendikten sonra İç Faktör Değerlendirme ve Dış Faktör Değerlendirme matrisleri ile SWOT analizinde ortaya konan faktörler ağırlıklandırılarak puanlanmıştır. Oluşturulan puanlar, İç-Dış Faktörler matrisine yerleştirilerek işletmenin bulunduğu stratejik konum tayin edilmiştir. Son aşamada ise, seçilen stratejiye ulaşmak amacı ile Kurumsal Karne (Balanced Scorecard-BSC) yönteminden faydalanılarak oluşturulan stratejik harita üzerinde faktörler arası ilişkiler gösterilmiş ve alt stratejiler belirlenmiştir.
Citation - WoS: 5
Citation - Scopus: 9
Predicting Cash Holdings Using Supervised Machine Learning Algorithms
(Springer, 2022) Özlem, Şirin; Tan, Ömer Faruk
This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.
Citation - WoS: 30
Citation - Scopus: 36
Prioritization of Public Services for Digitalization Using Fuzzy Z-Ahp and Fuzzy Z-Waspas
(Springer, 2021) Ucal Sarı, İrem; Sergi, Duygu
In this paper, public services are analyzed for implementations of Industry 4.0 tools to satisfy citizen expectations. To be able to prioritize public services for digitalization, fuzzy Z-AHP and fuzzy Z-WASPAS are used in the analysis. The decision criteria are determined as reduced cost, fast response, ease of accessibility, reduced service times, increase in the available information and increased quality. After obtaining criteria weights using fuzzy Z-AHP, health care services, waste disposal department, public transportation, information services, social care services, and citizen complaints resolution centers are compared using fuzzy Z-WASPAS that is proposed for the first time in this paper. Results show that health care services have dominant importance for the digitalization among public services.
Qubo Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem
(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Guney, Evren; Ehrenthal, Joachim; Hanne, Thomas
The Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.
Citation - WoS: 9
Citation - Scopus: 9
Sequential Testing in Batches
(2017) Ünlüyurt, Tonguc; Shahmoradi, Zahed; Özluk, Özgur; Selcuk, Barış; Daldal, Rebi
We study a new extension of the Sequential Testing problem with a modified cost structure that allows performing of some tests in batches. As in the Sequential Testing problem, we assume a certain dependence between the test results and the conclusion. Namely, we stop testing once a positive result is obtained or all tests are negative. Our extension, motivated by health care applications, considers a fixed cost associated with executing a batch of tests, with the general notion that the more tests are performed in batches, the smaller the total contribution of fixed costs to the sequential testing process. The goal is to minimize the expected cost of testing by finding the optimal choice and sequence of the batches available. The resulting NP-hard model is a variation of the set partitioning problem. We propose various heuristic algorithms for the effective solution of the problem and then demonstrate the performances of the algorithms through extensive numerical experiments.
Stokastik Süreler İçeren Kapasite Kısıtlı Parti Büyüklüğü Belirleme Problemi
(EJOSAT - DergiPark, 2019) 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.
Citation - WoS: 8
Citation - Scopus: 8
Zaman Pencereli ve Değişken Başlama Zamanlı Bir Araç Rotalama Problemi için Sütun Türetme Temelli Matsezgiseller
(DergiPark, 2019) Küçükaydın, Hande
In this study, a vehicle routing problem with time windows is investigated, where the costs depend on the total duration of vehicle routes and the starting time from the depot for each vehicle is determined by a decision maker. In order to solve the problem, two column generation based mat-heuristics are developed, where the first one makes use of the iterated local search and the second one uses the variable neighbourhood search. In order to assess the accuracy of the mat-heuristics, they are first compared with an exact algorithm on small instances taken from the literature. Since their performance are quite satisfactory, they are further tested on 87 large instances by running each algorithm 3 times for each instance. The computational results prove that the mat-heuristic using the variable neighbourhood search outperforms the other one. Hence, this enables to obtain a good feasible solution in a very short time when it is not possible to solve large instances with an exact solution method in a reasonable CPU time.

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