A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

IF 4 3区 经济学 Q1 ECONOMICS
Chicheng Huang, Serhat Yüksel, Hasan Dinçer
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Abstract

This study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

Abstract Image

可再生能源项目中知识驱动流程优化的新型模糊模型
本研究旨在确定关键指标,以提高可再生能源项目基于知识的流程优化。在此背景下,引入了一个新颖的模糊决策模型,该模型分为两个不同阶段。第一阶段是利用基于量子图模糊粗糙集(QPFR)的多步骤权重评估比率分析法(M-SWARA),对投资决策中基于知识的流程优化决定因素进行加权。另一方面,第二阶段包括通过基于 QPFR 的相似性排序偏好技术(TOPSIS)方法,对可再生能源项目流程优化的投资备选方案进行排序。本研究的主要贡献在于对影响可再生能源项目绩效的信息因素进行了优先级分析。这种情况为投资提供了实施适当战略的机会,以提高这些投资的优化程度。结论是,质量是这些项目流程优化的最基本指标。通过使用质量更好的产品,可以提高这些项目的效率。创新在确保使用优质产品实现环境可持续性方面发挥着重要作用。由于新技术的出现,使用更有效、更创新的产品变得更加容易。这种情况也有助于提高能源生产过程的效率。此外,研究结果还表明,最合适的能源创新替代品是各种清洁能源。通过关注不同的清洁能源替代品,可以最大限度地降低能源生产中断的风险。换句话说,在这种情况下,气候条件对能源生产的负面影响可以大大降低。
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来源期刊
CiteScore
5.90
自引率
27.30%
发文量
228
期刊介绍: In the context of rapid globalization and technological capacity, the world’s economies today are driven increasingly by knowledge—the expertise, skills, experience, education, understanding, awareness, perception, and other qualities required to communicate, interpret, and analyze information. New wealth is created by the application of knowledge to improve productivity—and to create new products, services, systems, and process (i.e., to innovate). The Journal of the Knowledge Economy focuses on the dynamics of the knowledge-based economy, with an emphasis on the role of knowledge creation, diffusion, and application across three economic levels: (1) the systemic ''meta'' or ''macro''-level, (2) the organizational ''meso''-level, and (3) the individual ''micro''-level. The journal incorporates insights from the fields of economics, management, law, sociology, anthropology, psychology, and political science to shed new light on the evolving role of knowledge, with a particular emphasis on how innovation can be leveraged to provide solutions to complex problems and issues, including global crises in environmental sustainability, education, and economic development. Articles emphasize empirical studies, underscoring a comparative approach, and, to a lesser extent, case studies and theoretical articles. The journal balances practice/application and theory/concepts.
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