The hub location problem in public transport focuses on strategically placing transport hubs to enhance service delivery and operational efficiency. Rising urban populations and transportation demands necessitate effective public transport systems. The challenge is to identify optimal locations for new hubs while balancing objectives like minimizing operational costs and minimize maximum passenger travel time. This study proposes a bi-objective model that selects potential hub locations from an interurban public transport network using the Multi-Criteria Decision-Making (MCDM) method of TOPSIS. The first objective is to minimize transportation costs, hub construction costs, and route creation costs, while the second aims to minimize maximum passenger travel time. Given that this problem is NP-hard, the Lagrangian Relaxation (LR) algorithm is employed for medium and large network sizes, with computational results provided. The proposed method is validated using Mandl's and Sioux Falls network data, which are standard benchmark datasets in transportation and network optimization. Results indicate that the algorithm effectively determines optimal hub locations. This approach can be adapted for specific parameters, bringing the problem closer to real-world conditions and uncertainties.