Using a hybrid approach based on the particle swarm optimization and ant colony optimization to solve a joint order batching and picker routing problem

Order picking is the most costly activity in a warehouse, because it is labor-intensive and repetitive. However, research on order picking has mainly focused on either order batching or picker routing alone; both of which are NP-hard problems. Therefore, considering the characteristics of existing logistics centers, namely, that order products and items are few but diverse, picking vehicles in logistics centers are limited, and batch amounts have upper limits in carrying capacity, this study proposes an efficient hybrid algorithm for solving the joint batch picking and picker routing problem to determine the batch size, order allocation in a batch, and the traveling distance. The core of the hybrid algorithm is composed of the particle swarm optimization (PSO) and the ant colony optimization (ACO) algorithms. PSO finds the best batch picking plan by minimizing the sum of the traveling distance. ACO searches for the most effective traveling path for each batch. The experimental results show that the hybrid algorithm is more efficient in terms of both solution quality and computational efficiency as compared with the known optimal solution and the current practices in industry. This method would improve picking performance and allow customer demands to be met rapidly.