WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653

Browse

Filters

2010 - 201932020 - 20253

Settings

Subject: search.filters.subject.Decision Making

Search Results

Now showing 1 - 6 of 6
  • Conference Object
    Citation - Scopus: 1
    A New Multi-Agent Decision Making Structure and Application To Model-Based Fault Diagnosis Problem
    (Institute of Electrical and Electronics Engineers Inc., 2017) Leblebicioglu, M.K.; Zengin, Y.; Schmidt, K.W.
    A new hierarchical multi-agent decision-making structure has been proposed. There are two phases of the structure. The first phase is the construction phase where the decision making structure consisting of switching and classification agents is built on the training data set generated by the system scenarios. In construction phase, switching and classification agents are trained and made ready for decision-making. In the decision phase, which is the second phase, the class of the new data sample is decided. This process is carried out by the transmission of the data sample to the correct classifier agent by the switching agents and the classification by the classifier agent. The proposed structure is applied to a complex fault identification problem and a successful result is obtained. The structure is also adaptable to other big data decision making problems. © 2017 IEEE.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 12
    Prioritizing Critical Success Factors for Wind Turbine Suppliers: a Neutrosophic Hybrid Dematel and Anp Approach
    (Springer, 2025) Edalatpanah, S. A.; Sicakyuz, C.; Nourkhah, S. A.; Pamucar, D.
    The global energy landscape is experiencing a transformative shift toward sustainability, with wind energy becoming an indispensable clean energy source crucial for combating climate change. As the number of wind turbine suppliers increases, stakeholders face significant challenges in selecting the most suitable partners, which can directly impact project outcomes and overall efficiency. This study aims to systematically identify and prioritize the critical success factors for wind turbine suppliers, facilitating effective decision-making in supplier selection. To achieve this, we present an innovative hybrid Decision Making Experiment and Evaluation Laboratory Method and Analytic Network Process framework, enhanced by a neutrosophic fuzzy environment. Our findings reveal that key factors such as component price (C1) with a weight of 0.055, operation and maintenance cost (C3) with 0.049, and annual energy production (C5) with 0.042 play significant roles in the decision-making process. Among these, component price (C1) is the most critical factor, indicating that cost considerations are paramount in the decision-making process. Notably, the findings emphasize the importance of optimizing supplier relationships, particularly in terms of service quality and technical competence, to ensure successful project implementation and long-term sustainability in the rapidly evolving renewable energy market. This study provides essential insights for both academic research and industry practice. Academically, it fills a critical gap in the literature on wind energy supplier selection by presenting a novel methodology that can guide future research. Practically, it equips industry stakeholders with actionable data to optimize supplier relationships, ultimately enhancing project outcomes and long-term sustainability. These findings are particularly relevant for stakeholders in emerging markets, where cost management and supplier selection are critical to project viability. By optimizing supplier selection, this study contributes to the broader goal of achieving sustainability in renewable energy projects.
  • Article
    Citation - WoS: 31
    Citation - Scopus: 35
    Some Einstein Geometric Aggregation Operators for Q-Rung Orthopair Fuzzy Soft Set With Their Application in Mcdm
    (Ieee-inst Electrical Electronics Engineers inc, 2022) Ali, Rifaqat; Awrejcewicz, Jan; Siddique, Imran; Jarad, Fahd; Iampan, Aiyared; Zulqarnain, Rana Muhammad
    q-rung orthopair fuzzy soft sets (q-ROFSS) is a progressive form for orthopair fuzzy sets. It is also an appropriate extension of intuitionistic fuzzy soft sets (IFSS) and Pythagorean fuzzy soft sets (PFSS). The strict prerequisite gives assessors too much autonomy to precise their opinions about membership and non-membership values. The q-ROFSS has a wide range of real-life presentations. The q-ROFSS capably contracts with unreliable and ambiguous data equated to the prevailing IFSS and PFSS. It is the most powerful method for amplifying fuzzy data in decision-making. The hybrid form of orthopair q-rung fuzzy sets with soft sets has emerged as a helpful framework in fuzzy mathematics and decision-making. The hybrid structure of q-rung orthopair fuzzy sets with soft sets has occurred as an expedient context in fuzzy mathematics and decision-making. The fundamental impartial of this research is to propose Einstein's operational laws for q-rung orthopair fuzzy soft numbers (q-ROFSNs). The core objective of this research is to develop some geometric aggregation operators (AOs), such as q-rung orthopair fuzzy soft Einstein weighted geometric (q-ROFSEWG), and q-rung orthopair fuzzy soft Einstein ordered weighted geometric (q-ROFSEOWG) operators. We will discuss the idempotency, boundedness, and homogeneity of the proposed AOs. Multi-criteria decision-making (MCDM) is dynamic in dealing with the density of real-world complications. Still, the prevalent MCDM techniques consistently deliver irreconcilable outcomes. Based on the presented AOs, a strong MCDM technique is deliberate to accommodate the flaws of the prevailing MCDM approaches under the q-ROFSS setting. Moreover, an inclusive comparative analysis is executed to endorse the expediency and usefulness of the suggested method with some previously existing techniques. The outcomes gained through comparative studies spectacle that our established approach is more capable than prevailing methodologies.
  • Article
    Citation - WoS: 30
    Citation - Scopus: 35
    Extension of Einstein Average Aggregation Operators To Medical Diagnostic Approach Under Q-Rung Orthopair Fuzzy Soft Set
    (Ieee-inst Electrical Electronics Engineers inc, 2022) Rehman, Hafiz Khalil Ur; Awrejcewicz, Jan; Ali, Rifaqat; Siddique, Imran; Jarad, Fahd; Iampan, Aiyared; Zulqarnain, Rana Muhammad
    The paradigm of the soft set (SS) was pioneered by Moldotsov in 1999 by prefixing the parametrization tool in accustomed sets, which yields general anatomy in decision-making (DM) problems. The q-rung orthopair fuzzy soft set (q-ROFSS) is an induced form of the intuitionistic fuzzy soft set (IFSS) and Pythagorean fuzzy soft set (PFSS). It is also a more significant structure to tackle complex and vague information in DM problems than IFSS and PFSS. This manuscript explores new notions based on Einstein's operational laws for q-rung orthopair fuzzy soft numbers (q-ROFSNs). Our main contribution is to investigate some average aggregation operators (AOs), such as q-rung orthopair fuzzy soft Einstein weighted average (q-ROFSEWA) and q-rung orthopair fuzzy soft Einstein ordered weighted average (q-ROFSEOWA) operators. Besides, the fundamental axioms of proposed operators are discussed. Multi-criteria group decision-making (MCGDM) is vigorous in dealing with the compactness of real-world obstacles, and still, the prevailing MCGDM methods constantly convey conflicting consequences. Based on offered AOs, a robust MCGDM approach is deliberated to accommodate the defects of the prevalent MCGDM methodologies under the q-ROFSS setting. Based on the planned MCGDM method, a medical diagnostic procedure is implemented to recognize the nature of certain infections in different patients. The protracted model estimates illustrious score values to determine patients' health compared to prevailing models, which is more helpful for healthcare experts in identifying the severity of diseases in patients. Furthermore, an inclusive comparative analysis is accomplished to ratify the pragmatism and effectiveness of the proposed technique with some formerly standing methods. The consequences gained over comparative studies display that our established method is more proficient than predominant methodologies.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 4
    A New Systematic and Flexible Method for Developing Hierarchical Decision-Making Models
    (Tubitak Scientific & Technological Research Council Turkey, 2015) Beldek, Ulas; Leblebicioglu, Mehmet Kemal; Lebleb Iciog lu, Mehmet Kemal; Belde, Ulaş
    The common practice in multilevel decision-making (DM) systems is to achieve the final decision by going through a finite number of DM levels. In this study, a new multilevel DM model is proposed. This model is called the hierarchical DM (HDM) model and it is supposed to provide a flexible way of interaction and information flow between the consecutive levels that allows policy changes in DM procedures if necessary. In the model, in the early levels, there are primary agents that perform DM tasks. As the levels increase, the information associated with these agents is combined through suitable processes and agents with higher complexity are formed to carry out the DM tasks more elegantly. The HDM model is applied to the case study 'Fault degree classification in a 4-tank water circulation system'. For this case study, the processes that connect the lower levels to the higher levels are agent development processes where a special decision fusion technique is its integral part. This decision fusion technique combines the previous level's decisions and their performance indicator suitably to contribute to the improvement of new agents in higher levels. Additionally, the proposed agent development process provides flexibility both in the training and validation phases, and less computational effort is required in the training phase compared to a single-agent development simulation carried out for the same DM task under similar circumstances. Hence, the HDM model puts forward an enhanced performance compared to a single agent with a more sophisticated structure. Finally, model validation and efficiency in the presence of noise are also simulated. The adaptability of the agent development process due to the flexible structure of the model also accounts for improved performance, as seen in the results.
  • Article
    Citation - WoS: 32
    Citation - Scopus: 53
    A Technology Readiness Levels (Trls) Calculator Software for Systems Engineering and Technology Management Tool
    (Elsevier Sci Ltd, 2010) Cakmak, Tanyel; Altunok, Taner
    Turkish defense industry and policy makers seek effective and successful system development programs by implementing a validation mechanism to verify the maturity of new technologies being developed in national laboratories and industry. Technology Readiness Levels (TRLs) developed by NASA as a general metric of technology advancement and it has been widely accepted as a systems engineering and technology management metric tool. In order to explore the sufficiency of this tool, first of all, academic and applicable studies of army and civil organizations have been searched out and the lessons learned have been analyzed in this study. Thereafter, questionnaires of awareness of TRLs and TRL Calculator have been applied to defense firms in Ankara, and interviews held with the technology developers, firms' speakers and defense authorities. Finally, the applicable algorithm of TRL calculator has been recommended for Turkish defense industry. (C) 2009 Elsevier Ltd. All rights reserved.