Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Dengiz, BernaStart date: 2020

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Now showing 1 - 10 of 17
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    Optimization Of The Redundancy Allocation Problem: Genetic Algorithm And Monte Carlo Simulation With Discrete Events
    (JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2023-09-26) Sahin, Merve Uzuner; Dengiz, Orha; Dengiz, Berna
    The reliability optimization of a system with various problem-specific constraints is an important problem. The Redundancy Allocation Problem (RAP) is the design of new systems with higher reliability using redundant components in parallel arrangement. While improving the system's reliability, the cost is also on the upswing. It has been ensured that system designs with higher reliability at lower costs, where failure and repair are considered, can be obtained (Table A). The reliability of the system with nonrepairable components is lower than the reliability of the system with repairable components. Furthermore, the cost of the system with nonrepairable components is higher than the cost of the system with repairable components.Purpose:The aims of this study are modeling the dynamic behavior of a system considering increasing failure and repair rates, and finding the optimal repairable system design.Theory and Methods:This paper presents a Discrete Event Simulation (DES) model to estimate the system reliability considering increasing failure and repair rates, and a Genetic Algorithm (GA) to find the optimal repairable system design.Results:According to the results, system designs with higher reliability at lower costs, where failure and repair are considered, can be obtained. It has been found that systems with repairable components are more reliable and cheaper than systems with nonrepairable components. Conclusion:It is obtained that the optimal repairable system design with higher reliability at lower cost than the nonrepairable system design.
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    Fractional Universal Kriging Metamodel
    (2022) Balaban, Muzaffer; Dengiz, Berna
    In this study, a Kriging-based metamodel is proposed that can be used instead of the simulation model for complex problems where data generation with a simulation model may be costly. In this new model structure, which is proposed for cases where the drift function structure of the Universal Kriging meta-model is not known. A power function of the variables that can also take fractional values is used instead of the first and second order regression models used as the drift function in the Universal Kriging metamodel. The predictive power of this metamodel, which is called Fractional Universal Kriging metamodel, has been investigated by experimentally computational analysis. Validation analysis reveals that the Fractional Universal Kriging metamodels have superior predictive power with respect to Mean Squared Error and Maximum Squared Error performance measures. Thus, in the case that the input-output relationship of the simulation model can be expressed with a power function that includes the effects of higher order and different from the quadratic polynomial case, Fractional Universal Kriging metamodels are proposed as a new metamodel approach.
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    Development of a Decision Support System for Selection of Reviewers to Evaluate Research and Development Projects
    (2023) Kocak, Serdar; Ic, Yusuf Tansel; Sert, Mustafa; Atalay, Kumru Didem; Dengiz, Berna
    The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, "Word2Vec". Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.
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    A Genetic Algorithm for the Redundancy Allocation Problem with Repairable Components
    (2022) Sahin, Merve Uzuner; Dengiz, Orhan; Dengiz, Berna
    The complexity of the structures of modern engineering systems in the communication and electronic fields, the demand for high reliable system evaluation methods has increased. While improving the system's reliability, the cost is also on the upswing. Redundancy allocation is important approach for designing telecommunication systems. The reliability is increased by increasing number of redundant components in a system. The Redundancy Allocation Problem (RAP) is the design of new systems with higher reliability using redundant components in parallel arrangement. This paper presents a genetic algorithm (GA) with discrete event simulation (DES) to solve RAP with repairable components. The promising proposed approach is illustrated and investigated by some RAP benchmark/test problems involving repairable systems.
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    Traveling Repairmen Problem: A Biogeography-Based Optimization
    (2022) Uzun, Gozde Onder; Dengiz, Berna; Kara, Imdat; Karasan, Oya Ekin
    Traveling Repairman Problem (TRP), which is also known by names cumulative traveling salesman problem, the deliveryman problem and the minimum latency problem, is a special variant of Traveling Salesman Problem (TSP). In contrast to the minimization of completion time objective of TSP, the desired objective of TRP is to minimize the cumulative latency (waiting time or delay time) of all customers. In this paper, a generalized version of TRP with multi depots and time windows, namely Multi Depot Traveling Repairman Problem with Time Windows (MDTRPTW) is considered. A group of homogeneous repairmen initiate and finish their visit tours at multiple depots. Each customer must be visited exactly by one repairman within their provided earliest end latest times. Being a challenging Nondeterministic Polynomial-hard (NP-hard) optimization problem, exact solution approaches are not expected to scale to realistic dimensions of MDTRPTW. Thus, we propose a biogeography-based optimization algorithm (BBOA) as a metaheuristic approach to solve large size MDTRPTW problems. The proposed metaheuristic is analyzed in terms of solution quality, coefficient of variation as well as computation time by solving some test problems adapted from the related literature. The efficacy of the proposed solution methodology is demonstrated by solving instances with 288 customers within seconds.
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    Applications of Deep Learning Techniques to Wood Anomaly Detection
    (2022) Celik, Yaren; Guney, Selda; Dengiz, Berna; Xu, J; Altiparmak, F.; Hassan, MHA; Marquez, FPG
    Wood products and structures have an important place in today's industry. They are widely used in many fields. However, there are various difficulties in production systems where wood raw material is under many processes. Some difficulty and complexity of production processes result in high variability of raw materials such as a wide range of visible structural defects that must be checked by specialists on line or of line. These issues are not only difficult and biased in manual processes, but also less effective and misleading. To overcome the drawbacks of the manual quality control processes, machine vision-based inspection systems are in great of interest recently for quality control applications. In this study, the wood anomaly has been detected by using deep learning. As it will be a distinction-based method on image processing, the Convolution Neural Network (CNN), which is one of the most suitable methods, has been used for anomaly detection. In addition, it will be tried to obtain the most suitable one among different CNN architectures such as ShuffleNet, AlexNet, GoogleNet for the problem. MobileNet, SqueezeNet, GoogleNet, ShuffleNet among considered methods show promising results in classifying normal and abnormal wood products.
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    Analysis of the Robustness of the Operational Performance Using a Combined Model of the Design of Experiment and Goal Programming Approaches for a Flexible Manufacturing Cell
    (2023) Ic, Yusuf Tansel; Yurdakul, Mustafa; Dengiz, Berna; Sasmaz, Turgut
    A combined model of a 2(k) design of experiment (DOE) and goal programming (GP) approaches is presented to determine optimum levels of input variables and analyze their robustness for a multiobjective performance of a flexible manufacturing cell (FMC) in this study. Two main performance metrics, namely, manufacturing lead time (MLT) and surface roughness (SR), are considered performance outputs for the FMC. Machine sequence, robot speed, tool type, and material type are selected as the four input variables on the input side of the proposed model. The study shows that even with a limited number of experiments, one can determine optimum input levels for the multiobjective performance of the FMC and determine their robustness.
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    A Multi-Objective Mathematical Model for Level of Repair Analysis with Lead Times and Multi-Transportation Modes
    (2022) Bicakci, Ismail; Ic, Yusuf Tansel; Karasakal, Esra; Dengiz, Berna; https://orcid.org/0000-0001-9274-7467; AGE-3003-2022
    In the event of failure of the product, level of repair analysis (LORA) is used to determine (1) whether the defective component should be discarded or repaired and (2) where this repair is made. In the literature, these repair operations are made with the aim of minimizing the total life cycle cost of the product. In this paper, we develop a multi-objective decision model that minimizes both the repair time (affected by lead times) and the repair costs. Our proposed model also considers the movement of the defective components to be performed by multiple transportation modes such as highway, railway, and airway. We use the epsilon constraint method to generate the Pareto frontier and analyze the trade-off between total repair costs and total repair time. We demonstrate the approach on an example problem.
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    Analysis of the manufacturing flexibility parameters with effective performance metrics: a new interactive approach based on modified TOPSIS-Taguchi method
    (2022) Ic, Yusuf Tansel; Sasmaz, Turgut; Yurdakul, Mustafa; Dengiz, Berna; 0000-0001-9274-7467; AGE-3003-2022
    Flexibility is one of the most important strategy parameters to achieve a long-term successful performance for a manufacturing company. Studies in the literature aim to operate a manufacturing system at optimum levels of flexibility parameters under its own manufacturing environment. This study aims to present an interactive analysis framework based on TOPSIS and Taguchi parameter design principles for investigating the effects of different levels of flexibility parameters on the performance of a flexible manufacturing cell (FMC). The main performance metric used in this study is manufacturing lead time. Other important metrics to evaluate quality control and inspection policies are also investigated in this study. To conclude, a combined model of an interactive approach based on TOPSIS and Taguchi methods are used to assess the effectiveness of the flexibility parameters for a FMC.