Hub Covering Location Problem Considering Queuing and Capacity Constraints

In this paper, a hub covering location problem is considered. Hubs, which are the most congested part of a network, are modeled as M/M/C queuing system and located in places where the entrance flows are more than a predetermined value. A fuzzy constraint is considered in order to limit the transportation time between all origin-destination pairs in the network. On modeling, a nonlinear mathematical program is presented. Then, the nonlinear constraints are converted to linear ones. Due to the computational complexity of the problem, genetic algorithm (GA), particle swarm optimization (PSO) based heuristics, and improved hybrid PSO are developed to solve the problem. Since the performance of the given heuristics is affected by the corresponding parameters of each, Taguchi method is applied in order to tune the parameters. Finally, the efficiency of the proposed heuristics is studied while designing a number of test problems with different sizes. The computational results indicated the greater efficiency of the heuristic GA compared to the other methods for solving the problem.

Hub Covering Location Problem Considering Queuing and Capacity Constraints Keywords:

Hub covering location , Queuing system , Congestion , Genetic algorithm , Hybrid particle swarm optimization algorithm