Endüstri Mühendisliği Bölümü Koleksiyonu

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

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Analysis of a New Business Model To Fundraise Non-Governmental Organizations Using Fuzzy Cognitive Maps
(IOS Press, 2020) Aytore, Can; Sergi, Duygu; Ucal Sari, Irema
Fundraising is one of the most critical issues for non-governmental organizations (NGOs) to carry out their projects. In this paper, a search engine project which aims to find additional financial sources and increase donations for NGOs is proposed. The proposed search engine project is analyzed using fuzzy cognitive maps (FCMs) to define and manage factor influences on the success of the project. FCMs are useful tools to define long term effects of important factors for a system. First casual relations of the factors are determined and then using sigmoid function for learning algorithm, the equilibrium state for the system is obtained. It is found that the factors generating monetary values are the most important ones for the project to be successful in long term.
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 - WoS: 49
Citation - Scopus: 52
Extension of Capital Budgeting Techniques Using Interval-Valued Fermatean Fuzzy Sets
(IOS Press, 2022) Sergi, Duygu; Sarı, İrem Uçal; Senapati, Tapan
Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFS s) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques.
Citation - WoS: 5
Citation - Scopus: 6
Generation of Feasible Integer Solutions on a Massively Parallel Computer Using the Feasibility Pump
(2017) Mehrotra, Sanjay; Koç, Utku
We present an approach to parallelize generation of feasible mixed integer solutions of mixed integer linear programs in distributed memory high performance computing environments. This approach combines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FP instances with different starting solutions concurrently, while allowing them to share information. Our computational results suggest that the improvement resulting from parallelization using our approach is statistically significant. (C) 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 20
Modeling of Carbon Credit Prices Using Regime Switching Approach
(2018) Çanakoğlu, Ethem; Ağralı, Semra; Adıyeke, Esra
In this study, we analyze the price dynamics of carbon certificates that are traded under the European Union's Emissions Trading System (EU-ETS). With the aim of investigating the joint relations among carbon, electricity, and fuel prices, we model historical prices using several methods and incorporating structural changes, such as econometric time series, regime switching, and multivariate vector autoregression models. We compare the results of the structural model with the results of traditional Markov switching and autoregressive models with breaks and present performance analysis based on the mean average percentage error, root mean squared error, and coefficient of determination. According to these performance tests, models with regimes outperform the approaches where breaks are defined using ex ante dummy variables. Moreover, we conclude that among regime switching models, univariate models are better than multivariate counterparts for modeling carbon price series for the analysis of both in-sample and out-of-samples. Published by AIP Publishing.
Citation - WoS: 1
Citation - Scopus: 2
Nonlinear Benefit-Cost Optimization-Based Selection of Insulation Material and Window Type: a Case Study in Turkey
(2017) Ağralı, Semra; Uctuğ, Fehmi Görkem
In this study, we maximize the energy savings of a hypothetical household by choosing an optimal insulation material with its optimal thickness and also the optimal window type. We develop a nonlinear mixed integer optimization model that maximizes the net present value of the benefits obtained by insulation over the lifespan of the house. Savings are calculated based on the gains from the electricity usage for air conditioning during cooling-required days and the fuel usage for heaters in heating-required days. The heat transfer calculations consider conductive, convective, and radiative components simultaneously. The optimization model takes the climate conditions of the region where the house is located, the consumer's desired indoor temperature, and the properties of the insulation options; then, it returns a combination of selected insulation materials with its optimum thickness and window type as output. We applied the optimization model developed to hypothetical houses in four different climatic regions of Turkey for different lifespans. For all reasonable lifespans, the model choses stonewool as the ideal insulation material. For high interest rates, single windows or double-glazed windows are preferable, but as the interest rate decreases and the net present value of the energy-savings increases, the model prefers triple-glazed windows as the ideal material. Erzurum, a city in climatic region 4, characterized by very cold winters and cool summers, was found to require the highest optimum insulation thickness, and the economic return resulting from the above-mentioned energy-saving actions was also found to be the highest in the case of Erzurum. In all the regions, the energy-saving investments were found to be feasible via applying the feasibility assessment techniques of net present value and payback period. The model developed in this study is applicable to any household as long as the required input data are available. Published by AIP Publishing.
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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