Dynamic mathematical hybridized modeling algorithm for e-commerce for order patching issue in the warehouse

The rapid expansion of E-Commerce globally, particularly in developing countries, necessitates innovative solutions to enhance warehouse efficiency. This research presents the Dynamic Mathematical Hybridized Modeling Algorithm (DMHMA), a groundbreaking approach designed to tackle warehouse order patching critical for optimizing order fulfilment processes. The DMHMA integrates advanced operational research techniques, specifically a tabu search (TS) algorithm, to effectively group orders for batching, thereby improving overall operational efficiency. Experimental results indicate that implementing DMHMA leads to a 25% increase in order-picking efficiency, significantly reducing the time required for order fulfilment. Additionally, the algorithm contributes to a 15% reduction in operational costs by optimizing the batching process and minimizing travel time within the warehouse. Furthermore, the application of DMHMA has been shown to enhance economic growth metrics by 20% in a Business-to-customer (B2C) warehouse context, demonstrating its potential impact on the broader e-commerce landscape.