Metaheuristic Optimization Algorithms in Multi-Level Supply Chains with a Focus on Innovation, STI, and Intellectual Property

This research presents an advanced study on the application of metaheuristic optimization algorithms to the design of multilevel supply chains, focusing on innovation, sustainability, and intellectual property integration. The case study addresses a Colombian logistics network managing eight product lines — six for the domestic market and two for export — under differentiated sourcing policies (single sourcing for exports and multiple sourcing for national demand). The model incorporates production plants, current and potential distribution centers, and strategic export ports, supported by data on demand, sales prices, production costs, storage, and transportation. Using a metaheuristic framework, the study achieved an optimal net profit of COP 16.9 billion, demonstrating the superiority of metaheuristics over deterministic methods in complex logistics scenarios. Key findings highlight Cali as the central production hub, Medellín as the dominant national redistribution center, and Buenaventura as the leading export port, validating the model’s robustness and strategic alignment with real-world constraints. This work positions metaheuristics as cutting-edge tools for Science, Technology, and Innovation (STI/CTI), advancing sustainable supply chain design, strengthening competitiveness and resilience, and emphasizing the role of intellectual property as a driver of long-term value creation in global logistics networks.