Carton Set Optimization in E-commerce Warehouses: A Case Study

Abstract

In this study, a three-stage methodology for carton set optimization in e-commerce warehouses is proposed and evaluated on three DHL Supply Chain warehouses. The methodology includes order cubing, carton grouping, and optimal carton set selection. A modified largest area fits first algorithm for order cubing is proposed. For optimal carton set selection, a genetic algorithm with a novel crossover strategy is introduced. The results show that the proposed carton set optimization approach can improve the shipping cost and carton utilization by 7% and 7.8%, and considerably improve the carbon footprint of the operations, even when the number of carton types is not changed.