Hub networks play a crucial role in optimizing transportation flow and reducing overall costs by efficiently connecting origins and destinations through strategically placed hub nodes. The decision of hub location carries significant long-term implications and necessitates consideration of various factors within an uncertain environment. This paper addresses the hub arc location problem in hub networks, considering setup costs, isolated hubs, and uncertain flows between nodes. To tackle this challenge, a two-stage stochastic programming model is formulated to incorporate the uncertainty in flow volumes. Additionally, a robust optimization approach is proposed to enhance the resilience of hub location decisions against uncertain scenarios. The problem is solved using a tailored Genetic algorithm, which achieves optimal solutions with high quality and reasonable computational time. The results demonstrate the effectiveness of the proposed methodology in handling the uncertain nature of the hub location problem, contributing to the advancement of transportation planning and logistics optimization. The findings provide valuable insights for practical applications in real-world scenarios, offering a framework for decision-makers to make informed choices regarding hub network design and location. By integrating uncertainty and robust optimization techniques, this paper offers a comprehensive approach to address complex transportation network problems and improve overall efficiency in transportation systems.
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