A novel multi-objective model for hub location problem considering dynamic demand and environmental issues

Hubs facilitate aggregation of connection, and switching points of material and people flow to reduce costs as well as environmental pollution. Hub Location Problem (HLP) is a relatively new research field of classical location issues. In this regard, this paper provides a tourist hub location problem to procure essential commodities, which characterized with non-negligible dynamics of demand. Dealing with a high level of change in demand for these goods over time, the possibility of establishment, renovation, or renting the distribution centers have been formulated in the proposed mathematical model. Finding the best location for distribution centers, the model aims to minimize the routing cost between production centers and retailers, along with emitting pollution from vehicles as less as possible. As the proposed model is bi-objective, that is minimizing costs and pollution emission, two Pareto-based solution methodologies, namely the non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), are used. Since the obtained results from these algorithms are highly dependent on the value of parameters, the Taguchi method is adopted to tune the parameters of two solution methodologies. Finally, to verify the proper performance of two solution methodologies, numerical examples in different scales are generated. The obtained results from all scales and solution methodologies indicate that the new modeling approach to the possibility of establishment, renovation, or renting the distribution centers results in lower costs and pollution emission. The results indicate that supply chain costs and environmental impacts increase by increasing the demand. The number of established distribution hubs also increases by increasing the demand.