A Genetic Particle Swarm Optimization And Its Application To The Optimal Shipping Sequence Of Container

The rapid development of global economy and trade makes logistics scale expansion. Compared with the railway transport and air transport, ship transport has better comprehensive. So it has more important responsibility in trade transportation. Container transportation occupies very important position in the field of shipping. In the process of port production and transportation, container transposition can not be inevitable. It not only improves the yard operation cost, but also improves the yard operation cost. So, solving the problem of container transposition successfully has a great significance and value to increase the productivity of shipping and enhance the competitiveness of the port.Container loading sequence will directly affect the container transposition number. The question boils down to a combinatorial optimization problem with constraints. In the past, people use the branch and bound and some other method to solve the problem. The results have some defects such as local convergence. With the appearance of variety kinds of swarm intelligence algorithms, there are many new effective ways to design more reasonable container shipping order.This article uses the swarm intelligent optimization algorithm to solve the problem of loading sequence optimization. According to the given container stowage plan and the yard deposit characteristics, this paper establishes the mathematical model aiming at minimizing the number of container transposition firstly. Then, modifying the Particle Swarm Optimization (PSO), a Parallel Genetic Particle Swarm Optimization algorithm (PGPSO) is put forward. The basic idea is making use of two parallel sub-populations, each sub-group separately in accordance with the global version and local version of PSO for the evolution. In this way it can combine the advantages of two versions of PSO, i.e. with fast convergence and preventing from premature. In addition, the introduction of the crossover and mutation operator of genetic algorithm makes the algorithm suitable for the discrete combination optimization problems. To verify the performance of proposed algorithm, at first it applied to solve the classical function optimization problems which the optimal solutions are known. The results show its feasibility and effectiveness. On this basis, according to container loading sequence optimization problem, this paper uses shortest stack down principle and the optimization stack down principle into the two subgroups of the mentioned algorithm separately. The crossover and mutation strategy are given to solve the concrete problems. Especially in reference to the idea of heuristic algorithm, the given mutation strategy can effectively improve the perfor-mance of the proposed algorithm. Using the proposed algorithm to the mathematical model, the different sizes of actual loading sequence optimization problems can be solved. And the results are analyzed and compared. Studies show that, the proposed algorithm is effective for the loading sequence optimization problem. It can give good optimization results and the loading sequence scheme is satisfactory.The work of this paper can provide scientific basis for container ship shipment operation, so that terminal yard can save costs and improve efficiency. The research has certain theoretical significance and practical application value.