3d-container loading problem with a distribution plan based on hybrid quantum genetic algorithm

Abstract

This paper proposes a multiobjective optimization model of a three dimensional container loading problem(3D-CLP) considering the distribution plan and designs its hybrid quantum genetic algorithm (HQGA). The purpose of the model is to maximize the rate of space utilization and to adjust the loading structure through simple parameter initialization. A hybrid algorithm is put forward based on a quantum genetic algorithm, and spaces depict strategy based on the surfaces. For this reason, the algorithm introduced in this paper proposed a new quantum coding pattern. Compared to the traditional evolutionary algorithm, the quantum genetic algorithm has more powerful capacity in traversal search. Finally, an actual numerical experiment and a comparison test were illustrated to verify the efficient and stable performance of the proposed algorithm. The results have shown that the solution has a satisfactory, stable, compact effect and an adjustable loading structure.