Abstract
Due to the availability of Industry 4.0 technology, the application of big data analytics to automated systems is possible. The distribution of products between warehouses or within a warehouse is an area that can benefit from automation based on Industry 4.0 technology. In this paper, the focus was on developing a dynamic route-planning system for automated guided vehicles within a warehouse. A dynamic routing problem with real-time obstacles was considered in this research. A key problem in this research area is the lack of a real-time route-planning algorithm that is suitable for the implementation on automated guided vehicles with limited computing resources. An optimization model, as well as machine learning methodologies for determining an operational route for the problem, is proposed. An internal layout of the warehouse of a large consumer product distributor was used to test the performance of the methodologies. A simulation environment based on Gazebo was developed and used for testing the implementation of the route-planning system. Computational results show that the proposed machine learning methodologies were able to generate routes with testing accuracy of up to 98% for a practical internal layout of a warehouse with 18 storage racks and 67 path segments. Managerial insights into how the machine learning configuration affects the prediction accuracy are also provided.
Highlights
Industry 4.0 technology has the potential to impact the operations of many industries.big data and analytics and autonomous robots are the two main components of Industry 4.0 technology [1]
The solutions can be determined by using a mathematical solver an installed executable module. This requires high-performance hardware with high computational such as CPLEX; for a heuristic method based on the A-star algorithm, the solution is generated by an capacity, where the computational runtime varies according to the dimension of input data instance
If the solutions from the A-star algorithm have high accuracy when compared the solutions from the shortest-path model, the A-star algorithm is used due to the shorter runtime with the solutions from the shortest-path model, the A-star algorithm is used due to the shorter and the computing resources required by the automated guided vehicle
Summary
Industry 4.0 technology has the potential to impact the operations of many industries. Autonomous robots are increasingly used to automate routine tasks in many industrial applications and tasks in special areas that are harmful to operators. The transportation industry is one of the areas that has potential for the application of AI-based automation, especially in supply chains, where there are large amounts of transactional data at every stage. The advancement of AI and robotics technologies brings about new opportunities for improving the operations across a supply chain. With AI and robotics automation, organizations can collect and perform data analysis autonomously, which improves supply chain responsiveness. The efficiency of operations within a warehouse can be improved with the application of AI-based automation due to the high rates of movement of products. The system was used to improve the efficiency of warehouse operations [3].
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