Yazar "Ervural, Bilal" seçeneğine göre listele

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    A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0-1 knapsack problems
    (Pergamon-Elsevier Science Ltd, 2023) Ervural, Bilal; Hakli, Huseyin
    The Reptile Search Algorithm (RSA), inspired by crocodiles' hunting behavior, is a recently introduced nature -inspired algorithm. Although the original version of the RSA shows outstanding performance in optimizing continuous applications, it is not suitable for discrete optimization problems like 0-1 knapsack problems (0-1 KP). To extend RSA to binary optimization issues, binary RSA (BinRSA) is proposed in this study. A wide range of transfer functions (TFs), including the largely used s-shaped and v-shaped, and recently introduced z-shaped, u -shaped, and taper-shaped, are investigated in the proposed algorithm to map the continuous values into binary. In addition, a novel repair method is introduced to cope with infeasible solutions for 0-1 KP and discussed in detail regarding its efficacy in reaching the optimal solution. The proposed method is validated on three benchmark datasets with 63 instances of 0-1 KP. First, the impact of 25 different transfer functions under six categories on the performance of the proposed binary algorithm is thoroughly investigated, and the results indicate that the taper-shaped T1 transfer function is superior to the other variants of the BinRSA. Then, the effectiveness of the proposed BinRSA with T1 transfer function is compared with some well-known and state-of -art algorithms, including Harris hawks optimization (HHO), slime mould algorithm (SMA), and marine predators algorithm (MPA). The experimental results show that compared to other methods, BinRSA considerably increased the solution accuracy and robustness for solving 0-1 KP.
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    A cumulative belief degree approach for group decision-making problems with heterogeneous information
    (Wiley, 2019) Ervural, Bilal; Kabak, Ozgur
    In some complex group decision-making (GDM) problems, the information needing to be processed may be heterogeneous. This may involve consideration of objective and subjective criteria by experts who have their own particular set of criteria, their own preference format for assessing alternatives under these criteria, and who may themselves be assigned differing importance weights as experts. This paper presents a cumulative belief degree approach to cope with heterogeneous information in multiple attribute GDM problems. The proposed approach focuses to aggregate subjective expert assessments and objective criteria that are presented in various representation formats and scales. The methodology employs transformation formulae for several preference representation scales to belief structure, including 2-tuple representation, classical fuzzy sets, hesitant fuzzy sets, and intuitionistic fuzzy sets. Aggregation formulae are proposed to combine expert criteria evaluations and find a collective preference. A consensus degree is calculated for measuring the agreement between the experts. An illustrative example is presented to clarify the steps of the methodology, and validity of the approach is assured through comparative analysis with the existing methods.
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    A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach
    (Pergamon-Elsevier Science Ltd, 2023) Ervural, Bilal; Ayaz, Halil Ibrahim
    The Failure Mode and Effect Analysis (FMEA) is a widely used method that effectively identifies and prioritizes potential risks in a given system or process. However, traditional and modified versions of FMEA are often criticized for their subjective assessments, inadequate risk prioritization methods, and lack of consideration of the importance level of risk factors. To address these issues, this study introduces a data-driven FMEA approach. Specifically, the proposed approach utilizes data-driven risk factors to determine objective rankings of failure modes. This study uses the frequency and stability of failures, time and product loss cost due to failure as objective and data-driven risk factors. These factors enable a more precise description of the influence of risk factors on failure modes. The Modified Criteria Ranking Importance with Intra-criteria Correlation (M-CRITIC) method is employed to assign weights to the identified risk factors, which indicates their level of importance in analysis. Additionally, the recently proposed Alternative by Alternative Comparison (ABAC) method is used to derive the risk priorities of failure modes. The effectiveness and applicability of the developed approach are demonstrated through a case study focused on manufacturing process risk analysis in the food industry. Furthermore, this study contributes to the growing trend toward objective risk calculations for FMEA and highlights the importance of using data-driven models for risk analysis.
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    A Hybrid Fuzzy Rule-Based Polyhedral Separation Approach: Medical Diagnosis Application
    (Springer International Publishing Ag, 2022) Ayaz, Halil Ibrahim; Ervural, Bilal
    Discrimination of two linearly inseparable sets using hyperplane(s) is considered one of the most successful classification methods. There is an expanding body of literature that realize the significance of classification of data accurately. Particularly, medical applications of classification provide meaningful early diagnosis results. However, medical datasets in real-world applications possess some noise and uncertainty. Therefore, dealing with uncertainty is a critical factor for accurate diagnosis. To overcome mentioned drawbacks, this study presents a hybrid fuzzy rule-based robust linear programming (RLP) and h-polyhedral separation (h-PolSep) approaches for breast cancer diagnosis through several consequent stages. In the proposed two-stage model, firstly the data is relabeled according to the fuzzy rule-based system, then the new outputs and original input values are classified using RLP and h-PolSep methods. Input fuzzification, generating membership functions, extracting fuzzy rules, and output defuzzification are examined in detail. A frequently used real-world medical dataset from the UCI Repository: the Wisconsin breast cancer is employed to evaluate the effectiveness of the classification using a number of metrics. Finally, the results demonstrate that the proposed approach effectively handles medical data classification problems.
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    Long-term policy evaluation: Application of a new robust decision framework for Iran's energy exports security
    (Pergamon-Elsevier Science Ltd, 2018) Alipour, Mohammad; Hafezi, Reza; Ervural, Bilal; Amin, Mohamad; Kabak, Ozgur
    The objective of this research is to assess long-term energy security policy under uncertain environment. Uncertainty is an integral part of the energy policy analysis in long-term planning, in particular for energy-exporting countries seeking to secure sustainable export revenues. This study proposes a framework to evaluate energy export policy at the strategic level by addressing inherent uncertainties exist in energy-exporting countries. Seven criteria (political, economic, social, technological, legal, environmental, and robustness) are considered to appraise the identified energy export security alternatives. A new hybrid Multi-Criteria Decision-Making (MCDM) model is proposed based on intuitionistic fuzzy sets suitable for uncertain judgments that integrates Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) and the Cumulative Belief Degree (CBD) methods. CBD, which is strengthened by IFAHP in determining criteria weights, allows experts to freely evaluate alternatives in various formats and can successfully deal with missing judgments by experts in case of doubt, eligibility or lack of information. Scenario planning is also incorporated into the decision-making process by determining four realistic projections. As a case study, the proposed framework is applied to analyze Iran's energy export security. Results suggest that natural gas has the highest export priority while petroleum products (excluding gasoline) stand last in all scenarios. (C) 2018 Elsevier Ltd. All rights reserved.
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    A maritime safety on-board decision support system to enhance emergency evacuation on ferryboats
    (Routledge Journals, Taylor & Francis Ltd, 2019) Sarvari, Peiman Alipour; Cevikcan, Emre; Celik, Metin; Ustundag, Alp; Ervural, Bilal
    Planning emergency evacuation operations in a proactive manner in public marine transportation systems is a critical success factor for passenger and crew safety. Despite the fact that there is a growing attention on safety issues for marine transportation systems, providing a real-time decision support for evacuation planning under different emergency conditions has not yet been addressed. In this context, this paper contributes to the related literature by providing a comprehensive methodology including simulation and statistical analysis along with a three-module decision support system (DSS) for ferryboat emergency evacuation. Emergency evacuation and fire environment are simulated via Maritime EXODUS V5.1 and SMARTFIRE V4.3, respectively. The methodology is applied to a real-life Ro-Ro ferry, and the results not only revealed significant factors on emergency evacuation performance, but also demonstrated the validity of the developed decision support system.