An Integrated Path-Planning and Slotting Optimization Model for AMR-Enabled High-Density Warehousing

This review examines the emerging convergence of path-planning algorithms and storage slotting optimization within Autonomous Mobile Robot (AMR)-enabled high-density warehousing systems. As modern fulfillment centers transition toward automation to meet escalating e-commerce demands, the coordination between dynamic navigation and intelligent inventory placement has become a critical determinant of operational efficiency. This paper synthesizes recent advances in multi-agent path planning, heuristic and metaheuristic slotting strategies, and integrated optimization frameworks that jointly address travel time minimization, congestion reduction, and order fulfillment speed. Particular attention is given to hybrid models that combine graph-based routing techniques, reinforcement learning, and real-time data analytics to adapt to stochastic warehouse conditions such as fluctuating demand, variable picking frequencies, and dynamic obstacle environments. The review further explores the role of digital twins and simulation-driven optimization in enabling predictive decision-making, as well as the integration of Internet of Things (IoT) data streams for continuous system feedback and recalibration. In addition, the paper evaluates trade-offs between centralized and decentralized control architectures, highlighting their implications for scalability, robustness, and computational complexity. By critically analyzing existing literature, the study identifies key research gaps, including the limited coupling of slotting and routing decisions in real-time environments, challenges in multi-objective optimization under uncertainty, and the need for standardized benchmarking datasets. The paper also discusses practical implementation considerations, such as system interoperability, energy efficiency, and safety constraints in human–robot collaborative settings. Ultimately, this review aims to provide a comprehensive foundation for the development of integrated optimization models that enhance throughput, reduce operational costs, and improve responsiveness in next-generation high-density warehouses.