Optimizing smart warehousing with genetic algorithm and Deep Q-Learning

This study uses Genetic Algorithm (GA) and Deep QLearning (DQL) methods to optimize the Order Picking Problem (OPP) and Storage Location Assignment Problem (SLAP) for Automatic Storage and Retrieval Systems (AS/RS). Dynamic storage policies based on order frequency are explored in the study, and the best optimization method is determined. The results show that the GA has lower order picking times and costs than the random assignment technique, while the DQL model increases the efficiency of operations through creating dynamic location policies. The study also shows that dynamic dwell points improve order picking time by 44% compared to fixed dwell points. These findings imply that logistics performance can be enhanced considerably by optimizing the location and dwell policies for warehouse managers.