Palletization Robot for an Industry 6.0 Revolution using AI for Smart Technology

Abstract

The Pallet Loading Problem (PLP), a complex and computationally intensive combinatorial optimization challenge which has been extensively considered across multiple industrial domains, especially in warehousing and logistics. In recent developments mainly focuses on solving multi-dimensional PLPs, by addressing constraints such as space utilization, weight distribution and load stability. This review synthesizes heuristic, meta-heuristic, and accurate algorithmic strategies includes genetic algorithms (GA), greedy methods, branch-and-bound techniques and hybrid models which tackles both homogeneous and heterogeneous item configurations accordingly. Prominently it focuses on by placing on robotics-integrated systems, machine learning (ML) enhanced detection mechanisms, and vision-based automation for the real-time pallet quality control and defects identification. In addition, the combination with flexible manufacturing systems (FMS), dynamic storage reallocation and digital twins illustrates the shift towards the smart manufacturing. Studies exerts the computational fluid dynamics and thermodynamic modelling discloses the critical insights into cold chain management and ergonomic considerations. This paper also explores innovative techniques aimed at minimizing errors, including pallet re-identification systems that use neural networks, and multi-objective optimization models that strike a balance between cost, environmental impact, and operational constraints. Overall, these advancements highlight the growing integration of artificial intelligence, cyber-physical systems, and modern logistics strategies—reflecting a strong shift toward smarter, more adaptable palletization systems that align with the goals of Industry 4.0 and intelligent supply chain management.