Integrating metaheuristic methods and deterministic strategies for optimizing supply chain equipment design in process engineering

In recent years, the escalating use of supply chains in process engineering has highlighted the need for efficient regional resource distribution. Traditionally, supply chains are designed through deterministic optimization models. However, these models require significant simplifications to mathematically represent the equipment design in these supply chains because of the complexity and nonlinearity these formulations bring to the problem, often leading to suboptimal solutions. This work proposes a novel methodology integrating deterministic and metaheuristic optimization techniques to address this challenge comprehensively. By combining these methods, the approach optimizes supply chain logistics and equipment design, enhancing overall performance, cost-efficiency, and sustainability. A case study on the polystyrene supply chain in Mexico demonstrates the effectiveness of our strategy, showcasing significant economic, environmental, and operational benefits. Two different schemes were used, a direct separation sequence and an indirect separation sequence. The results show that the direct separation sequence is better both economically and environmentally, due to the high energy consumption of the indirect separation. This integrated approach offers a robust mathematical tool for decision-making, setting a new standard in supply chain optimization.