Simulation-Based Optimization for Supply Chains with Multi-criteria Target Systems - A Comparison of Multiple Algorithms

The increasing importance of sustainability in industrial operations necessitates the integration of environmental considerations alongside traditional economic metrics in supply chain optimization. This study presents a comprehensive comparison of three metaheuristic approaches for multi-criteria supply chain optimization: Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Surrogate-Assisted Grey Wolf Optimization (SAGWO). These methods are applied to optimize a three-tier supply chain network considering costs, energy consumption, and service level simultaneously. The research contributes to sustainability by demonstrating how environmental factors can be systematically integrated into supply chain decision-making processes, enabling companies to achieve eco-efficient operations while maintaining economic viability. Results show that SAGWO achieves superior performance by reducing computational time while maintaining solution quality, making it particularly suitable for complex real-world applications where sustainability and efficiency must be balanced.