# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 1 Introduction  
Currently, Industry 4.0 technology has the potential to impact the operations of many industries. As shown in Figure 1, big data and analytics and autonomous robots are the two main components of Industry 4.0 technology [1]. Autonomous robots are increasingly used to automate routine tasks in many industrial applications and tasks in special areas that are harmful to operators. Artificial intelligence (AI)-based automation is adaptive and has many practical applications in different industries. 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. As such, the processes involved can receive benefit from AI and robotics automation. 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. New breakthroughs in AI, robotics, and automation can accelerate material development, the flow of products, material handling, and quality control in an integrated environment [2]. 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. One of the famous warehouse robot systems is the Kiva system implemented at Amazon. The system was used to improve the efficiency of warehouse operations [3].  
The focus of this research was on developing an AI-based automation methodology for controlling the movement of automated vehicles within a warehouse. In a typical operation within a warehouse, the amount of daily product movement is huge, and, in general, the routes are determined on the basis of the experience of drivers. Dynamic obstacles can obstruct the movement through some path segments within a warehouse and could affect the routes that can be used by the drivers. A route-planning process with the consideration of dynamic obstacles is complex and cannot rely only on human experience. In this research, methodologies for determining routes that consider real-time obstacles are presented. The methodologies include an optimization model for routing decision, an A-star heuristic method, and machine learning models based on an artificial neural network (ANN). The novelty of this research can be summarized as follows:  
* This research presents a route-planning methodology based on an ANN. The ANN can be implemented on automated guided vehicles (AGVs) with limited computing resources. Once an ANN is trained, it can generate a route with minimal computational effort and runtime (less than a second).
* The considered route-planning problem is based on a practical warehouse environment that considers real-time obstacles.
* A route-planning system that collects the data for training an ANN and generates routes on the basis of real-time positions of obstacles is introduced.
* The proposed methodology is adaptive and scalable. It can be applied to other routing problems with different requirements. Heuristic methods can be used for generating routes for large-scale problems.
* This research presents managerial insights into how the configuration of an ANN affects the accuracy, and how the best configuration can be chosen.  
The organization of this paper is as follows: Section 2 presents a literature review related to motion planning and route planning. A mathematical model for routing decisions and an A-star heuristic are described in Section 3. An ANN model for dynamic route planning is presented in Section 4. The transportation environment used in this research is presented in Section 5. A dynamic routing system and computational results with managerial insights are presented in Section 6. The conclusions and possible future research are discussed in Section 7.