<![CDATA[This study develops a multi-criteria optimization model for green supply chain Management using an Adaptive Non-dominated Sorting Genetic Algorithm II (Adaptive NSGA-II). The research aims to achieve a balanced integration of economic efficiency, environmental sustainability, and system resilience amid global supply chain disruptions. Using an evolutionary multi-objective optimization approach, the model was tested on empirical data from sustainable manufacturing industries in Southeast Asia. The results demonstrate that the proposed model successfully reduces total operational costs by 18.7%, decreases carbon emissions by 22.4%, and enhances supply chain resilience by 27.5%. These findings indicate that Adaptive evolutionary algorithms can effectively address complex, dynamic supply chain problems, producing solutions that are both efficient and environmentally responsible. Moreover, this study contributes theoretically by bridging the concepts of green supply chain and resilient supply chain optimization, and practically by offering a strategic framework for industries to design Adaptive and sustainable operational systems. The integration of this model with emerging technologies such as the Internet of Things (IoT) and big data analytics is recommended to support real-time decision-making and long-term industrial sustainability.]]>
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