This paper introduces a streamlined sugarcane supply chain network (SSCN) aimed at efficiently managing the substantial by-products of the sugar industry, with the primary objective of seamlessly reintegrating them into the supply chain with minimal alterations. The effective management of these by-products is crucial for minimizing overall operational costs. To tackle the inherent complexity of supply chain challenges, commonly utilized metaheuristic techniques such as Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO). Validation of the proposed approach is carried out through a real-world case study, while hypothetical test scenarios are utilized to further underscore its reliability. The results highlight a promising balance between solution effectiveness and computational efficiency, underscoring the viability of the approach.