An Adaptive Parallel Genetic Algorithm And Its Application To The Optimal Stowage Of Container Ships

Maritime transport occupied the absolute share in the field of world trade and played a huge role in the economic development of countries in the world in current society. Container transportation has become one of the most important forms of maritime transport and is gradually moving towards globalization and automation. To improve operational efficiency and cost savings, the problem of container ship’s stowage has become the core competitiveness of related to container transportation and one of the key technologies and a research focus in the related industries and academia.The problem of container ship’s stowage problem can be attributed to a complicated nonlinear combinatorial optimization problem that arranges the container’s position on the ship reasonably on the premise of meeting a variety of constraints, making the number of container shifts indicator optimum. In the loading process, not only to consider the layout rule constraints, but also the ship’s safety constraints, it has NP-hard. This paper takes the Shanghai Ocean Shipping Company’s1700TEU full container ship "Bing He" a voyage of optimizing stowage as the engineering background, using solving strategy of swarm intelligent algorithm, gives its optimizing stowage plan, trying to research and development of the problem, improving the economic benefits of maritime container transport, make some beneficial exploration.Specifically, this paper considers the ship’s stowage layout rule constraints and ship safety properties such as stability, trim, strength constraint, the minimization of the number of container shifts as objective function, builds a mathematical optimization model of full container ship stowage that loading at one port and unloading at the other ports. Then, clear about the use of swarm intelligent algorithm strategy on the basis of summarizing related researches at home and abroad. However, the classic swarm intelligent algorithm, such as genetic algorithm, usually has the disadvantage of premature and not very fast convergence rate. To solve the problem of ship’s stowage, this paper present a series of improvements, including introducing the idea of multi-population parallel evolution, improved adaptive crossover and mutation operator, adopting adjustable rank-based selection operator based on geometric progression etc., put forward an efficient adaptive parallel genetic algorithm APGA and through the classic examples of function optimization and knapsack problem to verify the feasibility and effectiveness of the algorithm. On this basis, the proposed algorithm is used to "Bing He" a voyage of ship stowage optimization problem and obtained satisfactory stowage plan.Research work of this paper show that the proposed algorithm is effective for the problem of container ship’s stowage and it can obtain better solutions, the automatic and intelligent solution to the problem provides the reference and support. In addition, there are a lot of complicated combinatorial optimization problems in engineering, so this study has a certain theoretical significance and application value.