## 4 The Application of the Ontology System  
The proposed system is a user-friendly interface because instead of ontology-specific queries (DL Query, SPARQL Query), the user only needs to specify a JSON descriptor, which is an easy-to-read format for humans  
The optimization of VRPs has been characterized as one of the most important success stories of logistics and supply chain management, providing and facilitating optimal solutions for vehicle fleets in a wide range of real-life applications including in-plant logistics and external supply chain solutions. The optimal solution for VRPs in logistics systems is especially challenging because they are characterized as one of the most challenging combinatorial optimization problems of logistics design and supply chain management. While optimizing the VRPs of real-life logistics applications, the following important questions must be answered:  
* Number of vehicles, related material handling equipment (loading and unloading, packaging), and human resources (drivers, operators) required to fulfil the predefined demands of customers. * The capacity of vehicles and related material handling equipment. * Scheduling of vehicles, material handling equipment, and human resources. * Required qualification of human resources. * Structure, location, and relationship of objects in the supply chain (number of tiers, collection, and distribution centers, cross-docking facilities, suppliers, users, third-party logistics service providers). * Assignment of objects, locations, resources, and products. * Definition of delivery strategies. * Definition of objective functions (cost, capacity utilization, flexibility, efficiency, availability, environmental impact, GHG emission, sustainability) and constraints (capacity, time). * The use of the chosen collection, distribution, and delivery strategies to reduce costs and improve performance by decision-makers and policymakers in the logistics area and supply chain management. The system is illustrated in Figure 10, where the model architecture is motivated by the proposal of [PERSON], [PERSON], and [PERSON] [55]. The ontology system is created so, that it can be handled by two types of users. One of the users (the expert user), who has adequate IT and logistics knowledge, does the following: purpose, scope, and requirement identification, then concept collection, ontology creation, and analysis. This user accesses the ontology through the ontology editor, where the user can implement it. The other user is the end-user who has adequate logistical knowledge. They ask questions to the system using a Java program and a JSON descriptor. The query engine layer of the Java program generates SPARQL queries, thereby extracting information from the ontology, and the Java program responds to the user. The ontology layer consists of the RDF/OWL file itself and the reasoner. In the figure, we marked each layer in red and each operation in blue. In the following, the JSON descriptor is introduced, which allows the user to ask questions to the system. The ontology sample system (Figure 11) assists in the optimal organization of transport tasks. If the user knows what types of nodes to visit during the transport task, the system helps to tell which products can be transported to or from the node and what constraint and objective function components can be used during the transport task. If the user knows which products the delivery is limited to, the sample system helps to decide what type of vehicles can be used. If the type of vehicle is known during the delivery task, the system tells us in response what products can be delivered with the vehicle, and what constraint components and objective function components can be included in the optimization. If the system input parameter is a node vehicle type, the response is the type of possible products to be transported to and from the node, the vehicles, the possible vehicle routing components, and the possible objective function components.