A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities

Hubs act as intermediate points for the transfer of materials in transportation system. In this study, a novel p-mobile hub location–allocation problem is developed. Hub facilities can be transferred to other hubs next period. Implementation mobile reduce costs opening and closing hubs, particularly an environment with rapidly changing demands. On hand, movement reduces lifespan adds relevant costs. The depreciation cost must considered number movements hub’s assumed limited. Three objective functions are minimize costs, noise pollutions, harassment caused by establishment people, new that locates less populated areas. A multi-objective mixed-integer non-linear programming (MINLP) model To solve proposed model, four meta-heuristic algorithms, namely particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm (NSGA-II), hybrid k-medoids famous clustering NSGA-II (KNSGA-II), K-medoids MOPSO (KMOPSO) implemented. results indicate KNSGA-II superior algorithms. Also, case study Iran implemented related analyzed.