Endüstri Mühendisliği Bölümü Koleksiyonu

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

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  • Conference Object
    Dialogue Enhancement Using Kernel Additive Modelling
    (Institute of Electrical and Electronics Engineers Inc., 2015-05-01) Liutkus, A.; Kırbız, Serap; Cemgil, A. Taylan
    It 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.
  • Book Part
    Citation - Scopus: 9
    Analysis 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, Irem
    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.
  • Book Part
    Citation - Scopus: 3
    Selection of the Best Face Recognition System for Check in and Boarding Services
    (Springer, 2021-08-27) 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.
  • Article
    Citation - WoS: 54
    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.
  • Article
    Analysis of a New Business Model To Fundraise Non-Governmental Organizations Using Fuzzy Cognitive Maps
    (IOS Press, 2020-08-06) Aytore, Can; Sergi, Duygu; Ucal Sari, Irema; Sari, Irem Ucal
    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.
  • Conference Object
    Citation - Scopus: 28
    Fuzzy Capital Budgeting Using Fermatean Fuzzy Sets
    (Springer, 2020-07-11) Sergi, Duygu; Sarı, İrem Ucal
    Investment projects are mostly evaluated by capital budgeting techniques to measure their profitability. The parameters used in capital budgeting such as future cash flows, interest rate and useful life involves high uncertainty due to the lack of information for the future environment. Since the uncertainty involved in forecasting the parameters is mostly in high levels, fuzzy set theory could be used in the determination of capital budgeting parameters to handle uncertain information in the analyses. Fermatean fuzzy sets are one of the most recent extensions of fuzzy set theory which are capable to handle higher levels of uncertainties by assigning fuzzy parameters from a larger domain. In this paper, fuzzy capital budgeting techniques that are fuzzy net present worth, fuzzy net future worth and fuzzy net annual worth are extended using fermatean fuzzy sets. An illustration for the calculations is also presented.
  • Article
    Citation - WoS: 35
    Citation - Scopus: 36
    Large-Scale Influence Maximization Via Maximal Covering Location
    (Elsevier, 2021-02-01) 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.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 18
    Gradual Covering Location Problem With Multi-Type Facilities Considering Customer Preferences
    (Elsevier, 2020-09-01) Küçükaydın, Hande; Aras, Necati
    In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers at a given demand zone patronize different types of facilities is equal to one. However, among the same type of facilities they choose the closest facility, and the strength at which this facility covers the customer is based on two distances referred to as full coverage distance and gradual (partial) coverage distance. If the distance between the customer location and the closest facility is smaller (larger) than the full (partial) coverage distance, this customer is fully (not) covered, whereas for all distance values between full and partial coverage, the customer is partially covered. Both distance values depend on both the customer attributes and the type of the facility. Furthermore, facilities can only be opened if their revenue exceeds a certain threshold value. A final restriction is incorporated into the model by defining a minimum separation distance between the same facility types. This restriction is also extended to the case where a minimum threshold distance exists among facilities of different types. The objective of the retailer is to find the optimal locations and types of the new facilities in order to maximize its profit. Two versions of the problem are formulated using integer linear programming, which differ according to whether the minimum separation distance applies to the same facility type or different facility types. The resulting integer linear programming models are solved by three approaches: commercial solver CPLEX, heuristics based on Lagrangean relaxation, and local search implemented with 1-Add and 1-Swap moves. Apart from experimentally assessing the accuracy and the efficiency of the solution methods on a set of randomly generated test instances, we also carry out sensitivity analysis using a real-world problem instance.
  • 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
    An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)
    (Elsevier, 2020-02-01) 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.