Mühendislik Fakültesi / Faculty of Engineering

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

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Author: search.filters.author.Sahin, Merve Uzuner

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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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    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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    Performance enhancement of production systems using fuzzy-based availability analysis and simulation method
    (2020) Sahin, Merve Uzuner; Dengiz, Berna; Atalay, Kumru Didem
    Due to the complexity of today's engineering systems, accurate system performance analysis is important to obtain a more productive system design. This study introduces a new approach to obtain a new system design considering system availability to increase its productivity in a more consistent and logical manner. To obtain more productive system design, both fuzzy availability analysis and simulation are used together. The conventional simulation modelling can be used to analyse the system behaviour considering failure and repair data to predict system throughput. This approach solves the problems of scarce data for failures and repairs in the system. The fuzzy availability analysis is used to consider system failures and repairs based on fuzzy set theory. In other words, the integration of simulation results and fuzzy system availability allows for analysing system performance to improve system productivity with new system design even though system failure and repair data are scarce. This new approach has been applied to improve system productivity in a battery production company in Turkey. The analysis results show that this new approach is able to analyse system performance accurately and improve system productivity with a new system design. [Received: 26 November 2018; Accepted: 17 December 2019]