Optimization of automated cigarette storage picking based on FP-growth algorithm

Abstract

The individualized demand for cigarettes from customers is growing quickly with the rapid expansion of e-commerce, posing a significant challenge to the finished cigarette warehouse system. This study proposes an automated warehouse picking optimization method based on the FP-growth algorithm and an improved genetic algorithm. This method significantly improves warehouse operational efficiency by optimizing product combination packing, storage location of goods, and order batching. Specifically, the study introduced two mechanisms to improve the genetic algorithm and optimize the storage location of goods: a dynamic parameter adaptive mechanism and an evolutionary reversal operator. It also combined the improved K-means algorithm (IKMA) for order batching optimization. Compared with existing methods, this study achieved rapid convergence to an approximate optimal solution and significant reductions in warehouse inbound and outbound operations and picking time. These reductions were 16.8–32.0% and 33.2%, respectively. Compared to the other two methods, the enhanced genetic algorithm stabilized the total number of bin entries and exited after approximately 420 iterations of product location optimization. These results indicate that the method proposed in this study has significant advantages in responding to the rapid changes in order demand in the e-commerce environment, providing a new direction for warehouse management.