A pareto-based clustering approach for solving a bi-objective mobile hub location problem with congestion

Background: This paper introduces an enhanced multi-period p-mobile hub location model that accounts for critical factors such as service time, flow processing delays, and congestion impacts at capacity-constrained hubs. As (urban) transportation networks evolve, mobile hubs play an increasingly vital role in promoting sustainable logistics solutions and addressing complex operational challenges. By enabling the repositioning of hubs across periods, this model seeks to minimize overall costs, particularly in response to dynamic demand fluctuations. Method: To solve this problem, we propose a bi-objective optimization model and introduce a hybrid meta-heuristic algorithm tailored to this application. The algorithm involves a clustering-based technique for evaluating solutions and a refined genetic approach for producing new sets of solutions. Results: Various experiments have been conducted on the Australian Post dataset to evaluate the proposed method. The results have been compared with Multiple-Objecti-ve Particle Swarm Optimization (MOPSO) and Non-Domi-nated Sorting Genetic Algorithm (NSGA-II) using several performance evaluation metrics. Conclusions: The results indicate that the proposed algorithm can provide remarkably better Pareto sets than the other competitive algorithms.