An Integrated Model for Developing a Global Logistics Network Using Metaheuristic Algorithms

The globalization of economies has heightened the importance of efficient logistics networks, particularly in the post-COVID-19 era where supply chain resilience is paramount. This study focuses on designing logistics hub networks to enhance global shipment flow management. A hub-based structure is adopted, aligned with existing global logistics frameworks. The research addresses key challenges such as optimizing warehouse and hub locations to streamline cargo delivery. An integrated model is developed for route planning, incorporating multi-modal transportation and cost-time trade-offs. The study employs metaheuristic algorithms including Strength Pareto Evolutionary Algorithm (SPEA-II), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimization Algorithm to solve the proposed model. Detailed numerical analyses demonstrate the model's capability to generate optimal global solutions, underscoring its practical utility in real-world logistics management.