Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks
Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01623-9 |
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| author | Shougi S. Abosuliman Saleem Abdullah Nawab Ali |
| author_facet | Shougi S. Abosuliman Saleem Abdullah Nawab Ali |
| author_sort | Shougi S. Abosuliman |
| collection | DOAJ |
| description | Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine fuzzy sets are further developed, and they are subsequently made simpler to apply to more than two such sets. Also, a real multi-criteria decision-making problem has been formulated. The environment plays a very important role in our daily lives. We cause different types of pollution in our environment, and it has a bad impact on our lives. Air pollution is one of the various forms of pollution that is thought to affect the entire globe. Millions of people die due to air pollution, and industries are the main contributors to air pollution. To overcome air pollution, green supply chain management plays a vital role, but green supply chain management faces some barriers as well. According to the proposed model, $${\mathfrak{R}}_{1}$$ R 1 is the best alternative and green supply chain management faces financial problems more than other barriers and also provides strategies to overcome financial barriers. In addition, a comparative analysis develops to illustrate the reliability and feasibility of the suggested technique in relation to current techniques. |
| format | Article |
| id | doaj-art-7bdda63506fd4b5abd412408b111237c |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-7bdda63506fd4b5abd412408b111237c2025-02-09T13:01:15ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-01-0111211910.1007/s40747-024-01623-9Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networksShougi S. Abosuliman0Saleem Abdullah1Nawab Ali2Department of Supply Chain and Maritime Business, Faculty of Maritime Studies, King Abdulaziz UniversityDepartment of Mathematics, Abdul Wali Khan University MardanDepartment of Mathematics, Abdul Wali Khan University MardanAbstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine fuzzy sets are further developed, and they are subsequently made simpler to apply to more than two such sets. Also, a real multi-criteria decision-making problem has been formulated. The environment plays a very important role in our daily lives. We cause different types of pollution in our environment, and it has a bad impact on our lives. Air pollution is one of the various forms of pollution that is thought to affect the entire globe. Millions of people die due to air pollution, and industries are the main contributors to air pollution. To overcome air pollution, green supply chain management plays a vital role, but green supply chain management faces some barriers as well. According to the proposed model, $${\mathfrak{R}}_{1}$$ R 1 is the best alternative and green supply chain management faces financial problems more than other barriers and also provides strategies to overcome financial barriers. In addition, a comparative analysis develops to illustrate the reliability and feasibility of the suggested technique in relation to current techniques.https://doi.org/10.1007/s40747-024-01623-9Neural networkContinuous linear Diophantine fuzzy setContinuous linear Diophantine fuzzy neural networksGreen supply chain management |
| spellingShingle | Shougi S. Abosuliman Saleem Abdullah Nawab Ali Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks Complex & Intelligent Systems Neural network Continuous linear Diophantine fuzzy set Continuous linear Diophantine fuzzy neural networks Green supply chain management |
| title | Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| title_full | Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| title_fullStr | Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| title_full_unstemmed | Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| title_short | Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| title_sort | barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks |
| topic | Neural network Continuous linear Diophantine fuzzy set Continuous linear Diophantine fuzzy neural networks Green supply chain management |
| url | https://doi.org/10.1007/s40747-024-01623-9 |
| work_keys_str_mv | AT shougisabosuliman barriersandenhancestrategiesforgreensupplychainmanagementusingcontinuouslineardiophantineneuralnetworks AT saleemabdullah barriersandenhancestrategiesforgreensupplychainmanagementusingcontinuouslineardiophantineneuralnetworks AT nawabali barriersandenhancestrategiesforgreensupplychainmanagementusingcontinuouslineardiophantineneuralnetworks |