Improve the Firefly Algorithm to Solve the Order Batching Problem of the “Cargo-to-Picker” Picking System

The rapid expansion and extensive integration of modern e-commerce have dramatically escalated consumer expectations regarding the speed of order fulfillment. In response, advanced smart warehouse technologies have emerged as pivotal solutions for optimizing distribution efficiency. This study focuses on addressing the challenge of batch order allocation within Automated Guided Vehicle (AGV)-based “goods-to-person” picking systems. It investigates a decentralized storage strategy aimed at facilitating batch picking across multiple orders simultaneously, thereby enhancing the efficiency of AGV operations. The research develops an integer programming model designed to minimize total AGV movements during batch order allocation. To optimize this model, an enhanced Firefly algorithm is introduced, featuring a dynamically adjusted parameter to expedite convergence during random operations. Experimental validation underscores the efficacy of both the model and algorithm, demonstrating significant improvements in batch order processing efficiency.