AI-enhanced Digital Twin systems for warehouse logistics optimization: A review of challenges with solutions and future directions

Digital transformation is revolutionizing warehousing by integrating advanced technologies like Digital Twins, AI, and IoT. Digital Twins create virtual warehouse replicas for simulation and optimization, enhancing resource allocation and proactive problem-solving. AI-driven systems predict and optimize processes, while Automated Guided Vehicles (AGVs) streamline the movement of goods. IoT improves connectivity and data collection, supporting decision-making through machine learning and deep learning. Robots, utilizing reinforcement learning and computer vision, automate tasks like picking and packing, boosting efficiency and reducing errors. Additionally, anomaly and intrusion detection enhance security. This review highlights key advancements in path planning, task allocation, inventory management, and storage assignment, which improve operational effectiveness. It also addresses the lack of analysis on the economic impacts of digital transformation, particularly in cost reduction, return on investment (ROI), and customer satisfaction. The paper identifies research gaps, including the integration of sustainability, adaptation to dynamic environments, collaborative robots, and optimization of reverse logistics. This review provides a foundation for future research on the potential of digitalization to transform warehousing practices.