A Soft Time Window (VRP with Soft Time Window) [23] means that a time window was added to the product demands of each customer, however, in this case, customers can also be visited outside the time window; only then will the solution receive a penalty point. Stochastic (Stochastic VRP) [24] is a problem where the value of some factor of the system (e.g., customer demand, time window) is given with a probability distribution. Time Window (VRP with Time Window) [25] means that a time window has been added to the demands of each customer in the system; customers should be visited within the time window. Traffic Congestion (VRPs with Traffic Congestion) [26] means that during the system, the route time between nodes is not fixed, it varies depending on the traffic. The traveling Salesman Problem [27] is a case when a single salesman (vehicle) visits the cities (customers); the objective is to minimize the distance traveled by the vehicles. Two-Echelon (Two-Echelon VRP) [28] means that the system includes not only depots and customers, but also intermediate distribution points (satellites); the vehicles first transport the products from the depot to the satellites and then from there to the customers. In the case of the fuzzy multi-level multi-objective fractional decision-making problem (ML-MOFDM) [29] the fractional objectives are given with fuzzy numbers. This problem has multiple objective functions. The supply chain network (SCN) [30] objective is the cost-efficient moving of an item or administration from provider to client. Such a problem is the bi-level multi-objective supply chain model (BL-MOSCM) [30], where the customer demand and supply are uncertain. The assignment problem (AP) [31] is also related to the VRP. In this case, however, tasks must be assigned to people. The ordering must be done in the most ideal way at the lowest cost. The following are the best-known algorithms for the task: linear programming, swarm optimization, and the Hungarian algorithm. In the case of the fuzzy AP [31], the system contains fuzzy data. Another important optimization algorithm used to solve logistics tasks is the bi-level optimization problem (BL-OP) [32]. In the case of an intuitionistic fuzzy multi-objective fractional transportation problem (FIF-MOFTP) [33], the seller's supply, the customer's demand, and the transportation fee are given by fuzzy numbers. Ontology is a standard tool for knowledge representation. The term comes from philosophy, which means a systematic description of existence. An ontology describes a domain, its concepts, and the relationships between concepts. Its purpose is not only the presentation of information but the automatic processing of the information content. The most common general ontology language is the OWL, developed by the World Web Consortium (W3C). The OWL helps machine interpretation of web content to a greater extent than XML, RDF, and RDF Schema (RDF-S). The OWL also offers an extended vocabulary and formal semantics [34; 35]. Ontology defines the concepts necessary for the representation and description of the knowledge area and helps to unify their treatment. Ontologies are used by databases, applications, and users who need to work together in a particular area of knowledge (e.g., medicine, financial management, etc.). Ontologies contain the basic concepts of the knowledge area and their contexts in a computer-interpretable form. Because the Semantic Web requires ontologies with a degree of structure, it must specify the following categories [36]: classes (general concepts) in a wide range of subject areas, relevant relationships between things, and the properties (or attributes) that these things may have. Ontologies are formulated to make precise, clear, meaningful distinctions between classes, attributes, and relationships between things. The main role of ontology is to unify the concepts: conclusion, specialization, and validation [36]. The main components of the OWL are the classes, properties, and individuals. The classes in OWL contain sets of individuals.