Optimization Of Mixed Bulk And Group Cargos Container Loading Problem

Container loading problem has been an important issue in container shipping industry. It is a typical NP hard problem with high complexity. Therefore, finding an effective algorithm to solve the container loading problem with multiple constraints has the important practical significance.First of all, a simple introduction about the container loading problem is given, then on the basis of the analysis and comparison of the exact algorithms and heuristic algorithms in detail, the paper proposes a rule-based heuristic algorithm based on "block" and "space" for solving the mixed bulk and group cargos container loading problem with angle constraint. Firstly, choose a space as the current loading space, and then according to the size of this space, generate blocks of cargos sorted by the goods’priority preliminarily. Then stow the block making the maximum use of the loading space and cut the space. In addition, based on spatial cutting and "Try-Space", an effective algorithm of avoiding angles of container is designed which can make full use of the space around the angle. For complete shipment of certain item groups constraint an iterative method based on estimation of the number of groups is presented. It works by estimating the number of the loading group through comparing the volume of the free space and cargos in a single group and constantly adjusting the number through try loading until all the groups can be completely shipped. This algorithm can ensure complete shipment of the group items in the same container or in specified ones.120 test cases are generated randomly in order to verify the effectiveness of the algorithm, and the experimental results show that the algorithm is superior to the benchmark algorithm in computing stability and time. Additionally, practical tests for the problem considering angle constraint and complete shipment of certain item groups constraint are also designed respectively using international standard container and container angle data. The results shows that when the new constraints are taken into account, the space utilization will be decreased slightly but still remain at high levels and the computing time is the same, which verify the effectiveness and practicability of the avoiding angles algorithm and the groups loading iterative method.