# Ontology-Based Production Simulation with OntologySim
## 3 Related Work
### Literature Review  
In the literature, there are many approaches to integrate ontologies in production and simulation. The majority of approaches to date focus on using the ontology as a memory or database to build a simulation model. This is similar to handling the simulation data management with the help of an ontology, yet, the simulation itself remains unchanged. Based on a grounded theory literature review [35], a large set of papers dealing with both simulation and ontology or both digital twin and ontology in the domain of production research were identified. By manual analysis a total of 20 papers, the most relevant research approaches were selected (cf. Table 1), with priority given to approaches with higher Ontology Integration Levels and the requirement of including ontologies from a research perspective in production organization. In general, the existing approaches can be clustered as follows:  
* _Concepts/schemata for production simulation:_ This category is characterized by the fact that schemes and concepts for a simulation are presented, but the active use has not yet been implemented or has only been carried out for an example case. Some of the concepts differ greatly in scope and  
Figure 4: Examining the Ontology Integration Level for ontology-based production simulation. structure of the ontology. Thus, multi-agent approaches were designed, such as that by [PERSON] et al. [36], who define an agent-based approach to support logistics and production planning; and [PERSON] and [PERSON] [37], who store data based on domain-related predicates, such as machine structure and task-related predicates, such as scheduling. Additionally, there are concepts for the description of the factory layout [38] by ontological means. Furthermore, there are databases, such as ONKI [39], available, which hold production data sets and ontology schemata for production. * _Real world application (CPS, MES):_ These ontology approaches are characterized by the fact that a virtual representation of a production plant and a Manufacturing Execution System (MES) is extended by an ontology to maintain flexibility. In these approaches, the ontology often serves as a classic knowledge database. Examples are the use of ontologies for MES and OPC-UA interfaces [40] and a digital twin for a Cyber-physical Production system (CPS) [28]. A different approach is followed by the virtual factory data model presented by [PERSON] [41], which aims at representing factory objects, i.e., from products to machines, virtually, in a static way. * _External data source to facilitate easier production simulation start:_ These papers pursue the integration of different external data sources to create a uniform simulation data model through an ontology. It is noticeable for this type of concept that multiple ontologies are used for modeling [42; 43; 44]. The simulation models are either converted into executable models, as in [PERSON] et al. [42], or the ontology serves as a knowledge database adjunct to the simulation model [43; 44]. [PERSON] et al. [43] introduce an ontology-driven framework based on scheduling, simulation and optimization ontology. [PERSON] et al. [44] implement a framework, where multiple databases are integrated into multiple ontologies and then combined to one core ontology model. A hybrid approach focuses on modeling external data sources, a static virtual model of the factory and its history, presented by [PERSON] and [PERSON] [45], likewise uses ontologies and extends so far static representations with the system's history and evolution. Thus, these can be classified as a digital shadow [3]. An extension of this approach is described by [PERSON] et al. [46], who use this continuously synchronized, ontology-based virtual model to enable \"in situ\" simulation of future system behavior. Hence, the approach aims at enabling a foresighted digital twin [3]. * _Fully ontology-based simulations:_ A similar approach to the OntologySim is only provided by the paper of [PERSON] et al. [47].