This study focuses on a real-world case that is defined as an extension of the container loading problem (CLP) with weakly heterogeneous types of boxes. A company producing plastic cups wants to increase its efficiency in container loading operations. To solve the problem, a hybrid approach including a static rule to rank orders and integer nonlinear programming (INLP) models is developed. INLP models form box sizes according to the properties of the cups, and determine the position of the boxes in layers while simultaneously minimizing the layer-depth. The presented approach is tested on real-world cases and benchmark data sets. The results show that simultaneously forming boxes and solving the CLP increases the effectiveness of container loading operations.

本研究侧重于一个真实案例，该案例被定义为具有弱异构类型的盒子的集装箱装载问题 (CLP) 的扩展。一家生产塑料杯的公司希望提高集装箱装载作业的效率。为了解决这个问题，开发了一种混合方法，包括静态规则排序和整数非线性规划 (INLP) 模型。INLP 模型根据杯子的属性形成盒子尺寸，并确定盒子在层中的位置，同时最小化层深度。所提出的方法在真实案例和基准数据集上进行了测试。结果表明，同时形成箱子并解决 CLP 提高了集装箱装载操作的效率。