This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.

A new heuristic algorithm for the heterogeneous MCLP(Multiple Container Loading Problem) is proposed in this paper. In order to solve MCLP, this algorithm generates an initial solution by applying the new SCLP(Single Container Loading Problem) algorithm t

A new heuristic algorithm for the heterogeneous single container loading problem is proposed in this paper. This algorithm fills empty spaces with the homogeneous load-blocks of identically oriented boxes and splits residual space into three sub spaces st

A new heuristic algorithm for the heterogeneous single container loading problem is proposed in this paper. This algorithm fills empty spaces with the homogeneous load-blocks of identically oriented boxes and splits residual space into three sub spaces starting with an empty container. An initial loading pattern is built by applying this approach recursively until all boxes are exhausted or no empty spaces are left. In order to generate alternative loading patterns, the load-blocks of pattern determining spaces are replaced with the alternatives that were generated on determining the load-blocks. An improvement algorithm compares these alternatives with the initial pattern to find improved one. Numerical experiments with 715 test cases show the good performance of this new algorithm, above all for problems with strongly heterogeneous boxes.