Solving a Multi-Objective Hub Covering Location Problem with Congestion by a New Algorithm Considering a Queue Theory

Hub covering problem (HLP) is a very popular areas of research for its wide ranges of applications in different service or manufacturing industries. This paper considers a bi-objective hub covering location problem with congestion. The objectives minimize the total transportation cost and the total waiting time for all hobs, respectively. The resulted multi-objective decision-making problem is formulated as mixed-integer programming (MIP) model. To solve this presented model, multi-objective parallel simulated annealing (MOPSA) is proposed and its performance is compared with two other meta-heuristics; namely, particle sward optimization (PSO) and non-dominated sorted genetic algorithm (NSGA-II). The computational results are compared in terms of four criteria including quality, mean ideal distance, diversification and spacing metrics. The associated results indicate that the presented model can outperform the other two meta-heuristics in terms of the quality metric.