A Solution For Container Loading Problem Base On Genetic Algorithm

The problem of container loading optimization is one of the important issues in logistics and distribution. Container transport , which as one of the leading in logistics and distribution, has become an important process of logistics and distribution in a developing rapidly in contemporary era. One of the key point is that container loading problem, which how to improve the efficiency of container loading to reduce the logistics cost for increasing competitiveness of enterprise, have become the focus of attention of scholars from domestic and foreign. Container loading problem are NP-hard problem, that scholars put their almost focus on the study of artificial intelligence algorithms. However, intelligence algorithms has its limitations and shortcomings. Thus the point that to improve and make the intelligent algorithms applied to the carrying task has been taken in front of the scholars from various countries.This research is based on the general process of the genetic algorithm, study its characteristic in each iteration stage, combined with the best individual preservation strategy and the idea of Simulated Annealing Algorithm developed a new improved genetic algorithm. With the problem of container loading, first, obtained by a heuristic algorithm to Initial population of Genetic Algorithm, to overcome the relative blindness of Genetic Algorithm to make the search direction is relatively clear. Then, in the iterative process of Genetic Algorithm, according to the features in different stages of Genetic Algorithm, as well as idea of Simulated Annealing Algorithm and Elitist-reserving Strategy, creatively mixed the two methods at the same time introduced into the Genetic Algorithm, to overcome the shortcomings of the Genetic Algorithm, to making the advantages of Genetic Algorithm to the maximize enhancement.At first, this paper has done a review in container loading problem research. Analyze and compare the characteristics in a variety of methods, and pointed out the problem of methods。Second, by deeply analysis of the advantages and the disadvantages of Genetic Algorithm, to raise one based on combining and improved the Elitist-reserving Strategy and Simulated Annealing Algorithm in the algorithm framework. Finally, the realization of the system, and concert examples of this algorithm is verified.