Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm

Abstract

• Developing a mathematical model for the p -mobile hub location problem. • Proposing a genetic algorithm augmented by a local search to solve the proposed model in large instances. • Analysis of proposed genetic algorithm operators performance in the proposed problem. • Comparison of the proposed model with classic networks to show mobile hub effectiveness in the networks. • Sensitivity analysis to show the model validation as well as the model applicability. In this study, a new mobile p -hub location problem in a dynamic environment is proposed, where there are mobile facilities inside hub nodes that can be transferred to other nodes in the next period. Mobile facilities have a mobility feature and can be transferred to other nodes in order to meet demand. Using such facilities will save extra hub establishment and closing costs in networks. This approach can be used in some real-world applications with rapidly changing demand, such as mobile post offices or emergency medical service centers, because designing immobile hub networks may be less efficient. In addition, designing dynamic hub networks entails establishing and closing costs in different periods. The model also considers a mobility infrastructure of hub facilities. The numerical examples confirm that a mobile hub network is more efficient than an immobile hub network in a dynamic environment. The effect of different parameters on the model is analyzed to consider its applicability conditions. A genetic algorithm, along with tuned parameters and a simulated annealing algorithm, are proposed to solve the model in large instances. Proposing of a model considering mobility feature in the hub location networks, proving its efficiency and finally proposing a proper solution algorithm are main contributions of this study. The model and solutions algorithms were analyzed by more numerical instances using Australia post (AP) dataset.