# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Technological Solutions
#### 4.7.1 Robotics- and IoT-Based Solutions  
In the realm of logistics and manufacturing, the integration of advanced technologies is essential to meet the challenges of modern industry. This section explores how various studies have leveraged robotics, machine learning, and advanced algorithms to address complex problems like the 3D Bin Packing Problem (3D-BPP), the Pallet Loading Problem (PLP), and the Container Loading Problem (CLP). These innovative solutions align with the principles of Industry 4.0, emphasising automation, efficiency, and data-driven decision making.  
A study presented by [PERSON] et al. [48] proposes the use of robotics to tackle the 3D-BPP, PLP, and CLP. The authors' approach integrates a robotic arm and introduces the \"Jampack\" algorithm for efficient 3D bin packing. Furthermore, the Fault Recovery Module (FRM) ensures reliability by detecting and correcting packing errors, minimising the need for manual intervention. This automated solution embodies the core principles of Industry 4.0, emphasising technology-driven efficiency.  
In another study [35], robotics plays a central role in solving the 3D-BPP, CLP, and PLP. The authors method combines a vision system for object recognition, machine learning algorithms for optimal packing configuration, and autonomous robotic packing. This approach aligns with the Smart Factory concept, a cornerstone of Industry 4.0, by leveraging intelligent devices and data-driven decision making.  
The application of robotics and intelligent algorithms in IoT-based e-fulfilment systems echoes the core tenets of Industry 4.0, emphasising automation, connectivity, and data-driven decision making. These solutions illustrate the principles of cyber-physical systems, making them suitable for contemporary manufacturing environments [39].  
Numerous other studies explore the application of robotics to solve the CLP and its variants. For instance, [PERSON] and [PERSON] [49] propose a heuristic-based solution for the Online 3D-BPP, highlighting the simplicity of the solution. [PERSON] et al. [21] address the real-world challenges of handling non-standard and deformable boxes, demonstrating a robotic system capable of accommodating such complexities. [PERSON] et al. [45] combine Deep Reinforcement Learning (DRL) with advanced hardware technology, employing robotic arms equipped with suction-based end-effectors and vision feedback modules.  
In the study presented by [PERSON] et al., 2023 [PERSON] et al. (2023), items are automatically picked from a conveyor belt and placed in containers, showcasing the effectiveness of DRL-based packing algorithms. Finally, [PERSON] et al., 2021 [PERSON] et al. (2021) develop a sophisticated robotic system that combines DRL with a suction-based picking module and mounts the robot on an Automated Guided Vehicle (AGV), streamlining the transportation of items to target containers.  
In the dynamic landscape of Industry 4.0, the application of robotics, machine learning, and advanced algorithms has emerged as a transformative force in addressing intricate packing and loading challenges. These studies exemplify the synergy between technology and efficiency, underscoring the potential of Industry 4.0 to revolutionise logistics and manufacturing.