Mühendislik Fakültesi / Faculty of Engineering

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Author: search.filters.author.Dengiz, BernaSubject: search.filters.subject.Genetic algorithm

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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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    Crew Pairing Optimization Based On Hybrid Approaches
    (2013) Aydemir-Karadag, Ayyuce; Dengiz, Berna; Bolat, Ahmet
    The crew pairing problem (CPP) deals with generating crew pairings due to law and restrictions and selecting a set of crew pairings with minimal cost that covers all the flight legs. In this study, we present three different algorithms to solve CPP. The knowledge based random algorithm (KBRA) and the hybrid algorithm (HA) both combine heuristics and exact methods. While KBRA generates a reduced solution space by using the knowledge received from the past, HA starts to generate a reduced search space including high quality legal pairings by using some mechanisms in components of genetic algorithm (GA). Zero-one integer programming model of the set covering problem (SCP) which is an NP-hard problem is then used to select the minimal cost pairings among solutions in the reduced search space. Column generation (CG) which is the most commonly used technique in the CPP literature is used as the third solution technique. While the master problem is formulated as SCP, legal pairings are generated in the pricing problem by solving a shortest path problem on a structured network. In addition, the performance of CG integrated by KBRA (CG_KBRA) and HA (CG_HA) is investigated on randomly generated test problems. Computational results show that HA and CG_HA can be considered as effective and efficient solution algorithms for solving CPP in terms of the computational cost and solution quality. (C) 2011 Elsevier Ltd. All rights reserved.
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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.