Q-learning algorithm and molecular fuzzy multi-objective particle swarm optimization-based decision-making approach to circular economy-oriented investment alternatives for renewable energy technologies
Determining the most critical performance indicators of renewable energy technology investments is necessary for different purposes, such as successful strategic planning and correct identification and management of risks. However, there are limited studies in the literature in this context. This is...
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| Vydáno v: | Information sciences Ročník 718; s. 122378 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
01.11.2025
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| ISSN: | 0020-0255 |
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| Abstract | Determining the most critical performance indicators of renewable energy technology investments is necessary for different purposes, such as successful strategic planning and correct identification and management of risks. However, there are limited studies in the literature in this context. This issue demonstrates that there is a significant gap in the literature on this subject. Accordingly, the purpose of this study is to identify effective investment strategies to improve renewable energy technologies. The first stage is related to the constructing the balanced expert dataset using Q-learning algorithm. The next stage consists of weighting the criteria with molecular fuzzy cognitive maps. The final section ranks the alternatives with molecular fuzzy multi-objective particle swarm optimization. The main contribution of this study is the optimization of investment strategies with a novel decision-making model. Integration of molecular geometry into multi-criteria decision-making analysis provides various advantages, especially in modelling complex systems, such as more effective uncertainty management and implication the normalization process better. The findings denote that innovative solutions are determined as the most important criterion in increasing the performance of renewable energy technologies. Lifecycle efficiency is another important variable in increasing the performance of renewable energy technologies. Moreover, developing circular models of decentralized energy systems is found as the most essential circular economy-oriented investment alternative for renewable energy technologies. |
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| AbstractList | Determining the most critical performance indicators of renewable energy technology investments is necessary for different purposes, such as successful strategic planning and correct identification and management of risks. However, there are limited studies in the literature in this context. This issue demonstrates that there is a significant gap in the literature on this subject. Accordingly, the purpose of this study is to identify effective investment strategies to improve renewable energy technologies. The first stage is related to the constructing the balanced expert dataset using Q-learning algorithm. The next stage consists of weighting the criteria with molecular fuzzy cognitive maps. The final section ranks the alternatives with molecular fuzzy multi-objective particle swarm optimization. The main contribution of this study is the optimization of investment strategies with a novel decision-making model. Integration of molecular geometry into multi-criteria decision-making analysis provides various advantages, especially in modelling complex systems, such as more effective uncertainty management and implication the normalization process better. The findings denote that innovative solutions are determined as the most important criterion in increasing the performance of renewable energy technologies. Lifecycle efficiency is another important variable in increasing the performance of renewable energy technologies. Moreover, developing circular models of decentralized energy systems is found as the most essential circular economy-oriented investment alternative for renewable energy technologies. |
| ArticleNumber | 122378 |
| Author | Castillo, Oscar Dinçer, Hasan Deveci, Muhammet Wu, Qun Eti, Serkan Olaru, Gabriela Oana Yüksel, Serhat |
| Author_xml | – sequence: 1 givenname: Hasan orcidid: 0000-0002-8072-031X surname: Dinçer fullname: Dinçer, Hasan email: hdincer@medipol.edu.tr organization: School of Business, Istanbul Medipol University, Istanbul, Turkey – sequence: 2 givenname: Serhat orcidid: 0000-0002-9858-1266 surname: Yüksel fullname: Yüksel, Serhat email: serhatyuksel@medipol.edu.tr organization: School of Business, Istanbul Medipol University, Istanbul, Turkey – sequence: 3 givenname: Serkan orcidid: 0000-0002-4791-4091 surname: Eti fullname: Eti, Serkan email: seti@medipol.edu.tr organization: IMU Vocational School, Istanbul Medipol University, Istanbul, Turkey – sequence: 4 givenname: Gabriela Oana orcidid: 0000-0001-9486-3349 surname: Olaru fullname: Olaru, Gabriela Oana email: gabriela.olaru@medipol.edu.tr organization: School of Communication, Istanbul Medipol University, Istanbul, Turkey – sequence: 5 givenname: Muhammet surname: Deveci fullname: Deveci, Muhammet email: muhammetdeveci@gmail.com organization: Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34942 Tuzla, Istanbul, Türkiye – sequence: 6 givenname: Qun surname: Wu fullname: Wu, Qun organization: School of Mathematical Sciences, Anhui University, Hefei, Anhui 230601, China – sequence: 7 givenname: Oscar orcidid: 0000-0002-7385-5689 surname: Castillo fullname: Castillo, Oscar email: ocastillo@tectijuana.mx organization: Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico |
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| Keywords | Multi-objective particle swarm optimization Renewable energy technologies Molecular fuzzy sets Q-learning algorithm |
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