# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 2 Literature Review
### Route Planning  
[PERSON] and [PERSON] [14] showed that algorithms such as Dijkstra and A-star can be applied for both path planning and routing decisions. Many scenarios were created on the basis of an unstructured and unpredictable environment, where the robots were forced to face new situations in which decision-making was quite difficult. [PERSON] and [PERSON] [15] considered an integrated framework for combining A-star and a Dijkstra algorithm. The proposed algorithm was used to guide a mobile robot along the path between an origin and a provided destination with the ability to avoid collisions with obstacles. [PERSON] and [PERSON] [16] implemented an A-star algorithm for planning the movement of a humanoid robot. The proposed algorithm had the ability to find a new route if the destination was modified or if the robot failed to follow the assigned path. [PERSON] et al. [17] considered an open vehicle routing problem with the capacity limitation and distance constraints. A biased random-key genetic algorithm designed to solve the problem was benchmarked with three datasets todemonstrate the efficiency of the proposed algorithm. [PERSON] et al. [18] investigated a vehicle routing problem (VRP) that considered uncertainty in demand under a restocking policy. An integer L-shaped algorithm and various lower bound schemes were developed to determine the solutions for problems with up to 60 customers and four vehicles. [PERSON] et al. [19] studied an ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows. The model aimed to simultaneously minimize the total distribution costs and maximize the overall customer satisfaction. [PERSON] et al. [20] developed a neural network that could be used to determine a collision-free path in a large-scale dynamic environment generated from a dataset that was extracted from a Bellman-Ford algorithm and a quadratic program. The dataset from the Bellman-Ford algorithm showed better reliability for training the neural network due to the appropriateness of this algorithm in a discretized space. [PERSON] et al. [21] formulated a mixed-integer linear programming model for an open capacitated VRP with soft time windows. An optimization model and four metaheuristic algorithms were developed to solve the problem. The results showed that the evolutionary algorithm provided good-quality solutions for both the small-scale and the large-scale problems with practical runtime. [PERSON] et al. [22] addressed an environmental prize-collecting vehicle routing problem. A firefly algorithm based on coordinates (FAC) was designed to solve the model. The proposed algorithm was performed and tested with many instances from previous studies to demonstrate the promising performance of the FAC. A summary of the methods used in key related articles is shown in Table 1. According to the literature review, route-planning and path problems for AGV are active research areas related to Industry 4.0 technology. None of the research addressed an adaptive solution methodology for a route-planning problem within a warehouse that considers real-time obstacles. The present research focused on developing a solution methodology using ANN for a route-planning problem with real-time obstacles. A dynamic route-planning system is also proposed. The main contribution of this research is the development of an efficient route-planning methodology using a machine learning model that can be implemented on AGVs with limited computing resources. An ANN model that can be used to determine the parameters that specify the relationship between the input and output of a dynamic route-planning problem is introduced. This can then be applied to predict the route on the basis of different scenarios of input data.