Efficient resource allocation and distribution are essential for optimizing industrial supply chains and logistics networks. This paper presents a novel hybrid approach that combines genetic algorithm (GA) with game-theoretic analysis to tackle the complex, multi-objective challenges inherent in these systems. The proposed framework focuses on minimizing transportation costs and carbon emissions while optimizing routing from source locations through intermediate facilities to final demand points. The genetic algorithm effectively navigates the search space to identify near-optimal solutions, while the game-theoretic analysis models strategic interactions among stakeholders, promoting stable and equitable resource allocation. The proposed approach exhibits advantages as it enhances cost efficiency and reduces emissions. The results provide the practical implications for improving the efficiency and sustainability of logistics operations across various industries. This study contributes to the advancement of resilient and adaptive supply chains by offering a comprehensive optimization framework that leverages the strengths of both genetic algorithms and game theory.