Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste DOI Creative Commons
Joel Joaquim de Santana Filho, Arminda Paço, Pedro Dinis Gaspar

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4758 - 4758

Published: April 25, 2025

The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance reverse supply chains for solid waste (RSCSW) within a circular economy framework. However, existing literature lacks systematic assessment effectiveness these methods compared to traditional management practices. This study conducts review (SLR), following PRISMA guidelines P.I.C.O. framework, investigate how MCDA AI can optimize governance, operational efficiency, sustainability RSCSW. After collecting 1139 articles, 22 were selected used analysis. results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, genetic algorithms, enhance decision-making automation, reduce costs, improve traceability. Nevertheless, regulatory barriers technological challenges still hinder large-scale adoption. proposes an innovative framework address gaps drive evidence-based public policies. findings provide policymakers managers, contributing Sustainable Development Goals (SDGs) agenda advancements in governance.

Language: Английский

Artificial Intelligence and MCDA in Circular Economy: Governance Strategies and Optimization for Reverse Supply Chains of Solid Waste DOI Creative Commons
Joel Joaquim de Santana Filho, Arminda Paço, Pedro Dinis Gaspar

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4758 - 4758

Published: April 25, 2025

The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance reverse supply chains for solid waste (RSCSW) within a circular economy framework. However, existing literature lacks systematic assessment effectiveness these methods compared to traditional management practices. This study conducts review (SLR), following PRISMA guidelines P.I.C.O. framework, investigate how MCDA AI can optimize governance, operational efficiency, sustainability RSCSW. After collecting 1139 articles, 22 were selected used analysis. results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, genetic algorithms, enhance decision-making automation, reduce costs, improve traceability. Nevertheless, regulatory barriers technological challenges still hinder large-scale adoption. proposes an innovative framework address gaps drive evidence-based public policies. findings provide policymakers managers, contributing Sustainable Development Goals (SDGs) agenda advancements in governance.

Language: Английский

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