[1B5-GS-2] Machine learning: Industrial application

[1B5-GS-2-05] A Study on Reducing Shipping Cost in E-Commerce Logistics Through Practical Packaging Box Selection Methods

〇Kota Fukamachi¹, Kiri Miura¹, Naoki Kobayashi¹, Kenji Tanaka¹, Atsuyoshi Muta², Yasuyuki Mitsui², Kazuhiro Koike², Shota Nishioka² (1. The University of Tokyo, 2. ASKUL Corporation)

Keywords:bin-packing problem, genetic algorithm, e-commerce Logistics

Amidst the expanding e-commerce market, shipping costs are rising due to factors like fuel price hikes and driver shortages, prompting freight rate adjustments. Since shipping fees are often based on the total dimensions of packaging boxes, using smaller boxes is key for cost reduction. This packing challenge, known as the 3D-Bin Packing Problem, is difficult to solve due to its NP-hardness. As a result, numerous heuristic solutions have been proposed to tackle this problem. However, these often overlook practical operational constraints. Our study addresses this by formalizing conditions around placing similar items together and considering their weight. We developed an algorithm to choose the smallest feasible box from multiple options for product group. Applied to real e-commerce logistics data, it selected smaller boxes than current methods in 45% of orders, reducing shipping costs by 3.5%. This indicates that our method can effectively reduce shipping costs while adhering to practical packing rules.