Endüstri Mühendisliği Bölümü Koleksiyonu

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

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Citation - WoS: 2
Citation - Scopus: 5
Increasing Procurement Efficiency Through Optimal E-Commerce Enablement Scheduling
(Emerald Group Publishing Ltd., 2019) Özlük, Özgür; Cholette, Susan; Clark, Andrew G
Purpose: This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings. Design/methodology/approach: This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints. Findings: The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions. Originality/value: The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period; additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.
Citation - Scopus: 3
Selection of the Best Face Recognition System for Check in and Boarding Services
(Springer, 2022) Ucal Sarı, İrem; Sergi, Duygu; Kuchta, Dorota
Check-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.
Citation - WoS: 4
Citation - Scopus: 4
Perceptual Coding-Based Informed Source Separation
(2014) Girin, Laurent; Kırbız, Serap; Ozerov, Alexey; Liutkus, Antoine
Informed Source Separation (ISS) techniques enable manipulation of the source signals that compose an audio mixture, based on a coder-decoder configuration. Provided the source signals are known at the encoder, a low-bitrate side-information is sent to the decoder and permits to achieve efficient source separation. Recent research has focused on a Coding-based ISS framework, which has an advantage to encode the desired audio objects, while exploiting their mixture in an information-theoretic framework. Here, we show how the perceptual quality of the separated sources can be improved by inserting perceptual source coding techniques in this framework, achieving a continuum of optimal bitrate-perceptual distortion trade-offs.
Citation - Scopus: 9
Analysis of Intelligent Software Implementations in Air Cargo Using Fermatean Fuzzy Codas Method
(Springer, 2022) Sergi, Duygu; Sarı, İrem Ucal; Kuchta, Dorota
The 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.
Citation - WoS: 13
Quantifying the Grounding Probability in Narrow Waterways
(CRC Press, 2020) Özlem, Ş.; Altan, Y.C.; Otay, E.N.; Or, I.
The aim of this paper is to estimate the grounding probability of vessels while navigating in narrowwaterways. In this study, the grounding probability is modelled as a combination the geometric probability, defined as vessel being on a grounding course and the causation probability, defined as the probability that the vessel is unable to avoid a grounding while being on a grounding course. A mathematical model is developed to estimate the geometric probability where the causation probability is estimated through a specially designed Bayesian network. The Strait of Istanbul, one of the narrowest waterways in the world, is used as a test case. The resulting grounding and ramming accidents are 2.8 times the ship collisions. The most critical causes of grounding accidents are the machine failure, steering inadequacy and lack of pilot support, respectively. With different input parameters, the proposed approach may be applied to other narrow waterways. © 2020 Taylor and Francis Group, London.
Citation - WoS: 23
Citation - Scopus: 22
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.
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: 31
Citation - Scopus: 33
Large-Scale Influence Maximization Via Maximal Covering Location
(Elsevier, 2020) Güney, Evren; Ruthmair, Mario; Sinnl, Markus; Leitner, Markus
Influence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude.
Citation - WoS: 54
Citation - Scopus: 59
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: 61
Citation - Scopus: 65
Carbon Price Forecasting Models Based on Big Data Analytics
(Taylor and Francis Ltd., 2019) Çanakoğlu, Ethem; Ağralı, Semra; Yahşi, Mustafa
After the establishment of the European Union's Emissions Trading System (EU-ETS) carbon pricing attracted many researchers. This paper aims to develop a prediction model that anticipates future carbon prices given a real-world data set. We treat the carbon pricing issue as part of big data analytics to achieve this goal. We apply three fundamental methodologies to characterize the carbon price. First method is the artificial neural network, which mimics the principle of human brain to process relevant data. As a second approach, we apply the decision tree algorithm. This algorithm is structured through making multiple binary decisions, and it is mostly used for classification. We employ two different decision tree algorithms, namely traditional and conditional, to determine the type of decision tree that gives better results in terms of prediction. Finally, we exploit the random forest, which is a more complex algorithm compared to the decision tree. Similar to the decision tree, we test both traditional and conditional random forest algorithms to analyze their performances. We use Brent crude futures, coal, electricity and natural gas prices, and DAX and S&P Clean Energy Index as explanatory variables. We analyze the variables' effects on carbon price forecasting. According to our results, S&P Clean Energy Index is the most influential variable in explaining the changes in carbon price, followed by DAX Index and coal price. Moreover, we conclude that the traditional random forest is the best algorithm based on all indicators. We provide the details of these methods and their comparisons.
Column Generation Based Algorithms for a Vrp With Time Windows & Variable Departure Times
(2016) Michelini, S; Arda, Y; Küçükaydın, Hande
...
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).
Citation - WoS: 1
Citation - Scopus: 1
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 - Scopus: 1
Interval Valued Intuitionistic Fuzzy Z Extensions of Ahp&codas: Comparison of Energy Storage Alternatives
(Springer, 2023) Sergi, Duygu; Sarı, İrem Uçal
Energy storage technologies are receiving increasing attention due to the trend toward renewable energy sources. Energy storage systems are a promising technology as they provide the low carbon emissions needed in the future, contribute to renewable energy production, and offer an alternative to petroleum-derived fuels. It is not possible to say precisely how the energy will be stored, and often more than one method must be used together. In this study, battery technologies from electrochemical energy storage systems are discussed. This chapter proposes a multi-criteria decision-making (MCDM) model combining fuzzy IVIF-Z-AHP and fuzzy IVIF-Z-CODAS methods to choose the optimal battery ESS. The priority weights of 4 main and 11 sub-criteria related to energy storage efficiency are determined using the IVIF-Z-AHP method. After that, 5 different batteries are evaluated using the IVIF-Z-CODAS method, and the most appropriate battery ESS is selected by doing a performance evaluation regarding the storage of energy at maximum efficiency.
Citation - WoS: 6
Citation - Scopus: 5
Optimal Keyword Bidding in Search-Based Advertising With Budget Constraint and Stochastic Ad Position
(Taylor & Francis, 2019) 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.
Citation - WoS: 26
Citation - Scopus: 26
An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks
(Elsevier, 2019) Güney, Evren
The influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.
Combining Acceleration Techniques for Pricing in a Vrp With Time Windows
(2016) Michelini, S; Arda, Y; Küçükaydın, Hande
...
Customer Segmentation Using Rfm Analysis: Real Case Application on a Fuel Company
(Emerald Publishing Limited., 2020) Ucal Sarı, İrem; Sergi, Duygu; Ozkan, Burcu
Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.
Citation - WoS: 18
Citation - Scopus: 22
A Capacitated Lot Sizing Problem With Stochastic Setup Times and Overtime
(2019) 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.
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.

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