Endüstri Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942
Browse
39 results
Search Results
Article Citation - WoS: 1Citation - Scopus: 3Evaluation of Learning Management Systems Using Interval Valued Intuitionistic Fuzzy-Z Numbers(Anadolu Üniversitesi, 2023-10-01) Ucal Sarı, İrem; Sergi, Duygu; Sari, Irem UcalThe 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.Conference Object Dialogue Enhancement Using Kernel Additive Modelling(Institute of Electrical and Electronics Engineers Inc., 2015-05-01) Liutkus, A.; Kırbız, Serap; Cemgil, A. TaylanIt is a major problem for the sound engineers to find the right balance between the dialogue signals and the ambient sources. This problem also makes one of the main causes of the audience concerns. The audience wants to arrange the sound balance based on their personal preferences, listening environment and their hearing. In this work, a method is proposed for enhancing the dialogue signals in stereo recordings that consist of more than one source. The kernel additive modelling that has been used successfully in sound source separation is used to extract the dialogues and the ambient sources from the movie sounds. The separated dialogue and ambient sources can later be upmixed by the user to make a personal mix. The separation performance of the proposed method is evaluated on the sounds generated by mixing the sources which were taken from the only dialogue and only music parts of the movies. It has been shown that the Kernel Additive Modelling (KAM) based method can be successfully used for dialogue enhancement. © 2015 IEEE.Article Citation - WoS: 9Citation - Scopus: 15Mixcycle: Unsupervised Speech Separation Via Cyclic Mixture Permutation Invariant Training(IEEE, 2022) Karamatlı, Ertuğ; Kırbız, SerapWe 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).Article Citation - WoS: 5Citation - Scopus: 12Predicting Cash Holdings Using Supervised Machine Learning Algorithms(Springer, 2022-05-18) Özlem, Şirin; Tan, Ömer FarukThis 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.Article A Lot-Sizing Problem in Deliberated and Controlled Co-Production Systems(Taylor and Francis, 2022-02-11) Kabakulak, Banu; Ağralı, Semra; Taşkın, Z. Caner; Pamuk, BahadırWe 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.Book Part Citation - Scopus: 9Analysis of Intelligent Software Implementations in Air Cargo Using Fermatean Fuzzy Codas Method(Springer, 2021-08-27) Sergi, Duygu; Sarı, İrem Ucal; Kuchta, Dorota; Ucal Sari, IremThe chapter focuses on the problem of analyzing and selecting intelligent software in Air Cargo in the concept of Aviation 4.0. First, the notions, problems and challenges linked to air cargo are discussed. Recent developments, ongoing innovative projects and unfilled gaps in the area of intelligent air cargo software are presented. Next, the proposed method to analyze a select software to be used by air cargo companies is described. It is a modified version of one of the recent multi-criteria decision-making methods, called CODAS. Its original, crisp version and its existing fuzzy extensions are first presented. Next, an original extension of the method, using Fermatean fuzzy sets, is proposed. In the application section a logistics company is considered, which is facing the problem of selecting software supporting the air cargo process. The criteria are selected by experts holding various positions in the company, and three alternatives of air cargo software provider are determined. Then, the proposed method is applied to solve the intelligent software selection problem. Finally, conclusion and future research perspectives are given.Article Müşteri Hizmetleri Bölümünde Süreç Analizi ve Stratejik Planlama- Lastik Sektöründe Bir Uygulama(Eskişehir Teknik Üniversitesi, 2020-08-31) Özuduruk, Semih Faruk; Sergi, Duygu; Sarı, İrem UcalBu 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.Book Part Citation - Scopus: 3Selection of the Best Face Recognition System for Check in and Boarding Services(Springer, 2021-08-27) Ucal Sarı, İrem; Sergi, Duygu; Kuchta, DorotaCheck-in and boarding services are one of the most human oriented pre-flight services in aviation industry. The process of using face recognition systems increase with the aviation 4.0 concept, decreases need for manpower and increases the efficiency of the processes. Therefore, problems, developments and challenges of face recognition in terms of aviation 4.0 are discussed in this chapter to determine the best face recognition system for check in and boarding systems. Analytic hierarchy process and grey relational analysis are used to analyze current system providers. To handle the ambiguity in the linguistic evaluations, fuzzy Z- numbers are used. 10 face recognition system providers are evaluated according to five criteria with the proposed methodology and the results are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Article Citation - WoS: 54Citation - Scopus: 57Branch-And Methods for the Electric Vehicle Routing Problem With Time Windows(Taylor and Francis, 2021-07-31) Çatay, Bülent; Duman, Ece Naz; Taş, DuyguIn 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.Article Citation - WoS: 1Citation - Scopus: 2Determining and Evaluating New Store Locations Using Remote Sensing and Machine Learning(Tübitak, 2021-05-31) Ünsalan, Cem; Turgay, Zeynep Zerrin; Küçükaydın, Hande; Höke, BerkanDecision 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.