三维装箱问题, 聚类, 蚁群算法, 重心偏移量

To tackle the challenge of synergistically optimizing space utilization and center-of-gravity stability in loading highly heterogeneous goods, a novel 3D boxing algorithm is presented combining adaptive clustering and improved swarm intelligence optimization. It overcomes the limitations of traditional K-means and ant colony algorithm combinations by innovatively building a “ volume-weight" dynamic clustering mechanism, which optimizes cluster numbers via silhouette coefficients to enable collaborative goods grouping and initial loading sequence generation, resolving stacking instability from weight differences in similar goods. Further, an improved ant colony strategy with center-of-gravity constraints is designed, converting six-direction attitude selection into a pheromone-guided multi-dimensional optimization. By introducing a center-of-gravity compensation factor and spatial fitness heuristic function, collaborative optimization of loading attitudes and placement sequences is achieved. Experimental results show the algorithm effectively breaks the “ seesaw effect "between space utilization and center-of-gravity stability, offering a new technical path for intelligent loading of highly heterogeneous goods in logistics and a valuable collaborative optimization framework for complex combinatorial problems.

three-dimensional packing problem, clustering, ant colony algorithm, center of gravity offset