A constraint programming approach and a hybrid of genetic and K-means algorithms to solve the p-hub location-allocation problems

Abstract

ABSTRACT p-Hub location-allocation problem is one of the most interesting subjects in the location theory. Hubs act as switching points to reduce the transportation cost. In this study, two new solution methods, a constraint programming (CP) based model and a hybrid of k-means and genetic algorithm (KGA), are developed to generate exact and approximate solutions, respectively. The proposed CP formulation is more understandable and straightforward in comparison with the MIP model. The experimental results indicate that the CP model uses the memory of the computer (RAM) more efficiently, which enables us to solve the medium size problems. But, in terms of run time, this method cannot be superior to the MIP model. The CP formulation is also extended for the multi allocation p-hub location problem. K-means algorithm, a well-known algorithm for clustering data, is used to generate initial solutions of GA. Furthermore, a new adaptive crossover operator, which is based on the k-means algorithm, is proposed. The experimental results indicate that the KGA algorithm is superior to the GA, regarding time, objective value, and quality of solution measures.