## 5 Proposed Methodology
### Process Modelling Methodology
#### 5.3.2 Model Synthesis and Operation Procedure  
The methodology followed for the modelling of discrete processes is a modular one, as this serves the already mentioned use of hierarchical models. A module is a fundamental subsystem with a set of input and output connection arcs defining interactions with other modules and a set of internal discrete and continuous relations that define the module's internal hybrid state. By using a modular approach, it is possible to simplify the description, qualitative (properties and invariants) and quantitative (mean production cycle, WIP, etc.) analysis of a complicated system by splitting its complexity into small and simple entities that can also be considered and analysed independently of each other [35] and to overcome problems relative to the systems behaviour (e.g., some conflicts types) [36]. In addition, modular representation enables the reuse of the specified models and even the implementation of algorithms for automatic model synthesis with respect to system features [17]. According to the proposed approach, the following parts-levels are considered. * Material processing level represents the main part of the model. In this, the processes performed in order to transform raw materials in final products are represented. The entities composing the production system, such as machines, moving equipment, etc., are represented, and interactions between them are defined. Additionally, resources of the system are represented, and their use through the system is defined. * Control level is implemented by adding extra characteristics to the model. Certain quantitative parameters are defined (such as buffer capacities, process conditions, etc.) and ancillary structures are added in the model in order to control its behaviour. These parameters and structures constrain the model's behaviour and limit the state space (possible reachable states during simulation) within given limits. Typically, through the implementation of the ancillary model structure, sets of mutually exclusive resource states are defined, and through the prioritisation of certain actions, the behaviour of the system can be controlled to follow given constraints. * Decision making is implemented through the definition of quantitative parameters and the addition of auxiliary structures in the model. Such typical decisions concern priorities between the occurrence of certain events when conflicts are met, the performance of the schedule calculated from optimisation tools and resource allocation through time. The use of an inhibitor and test arcs can model the performance of events concerning, for example, change of the system state when buffer levels increase or decrease with respect to a given limit. The overall Petri net model building procedure is depicted in Figure 2. Currently, it can be used for the synthesis of Petri net models of production systems that can be partially or totally considered production lines or have parallel machines that can perform the same type of processes in the given stages of the production procedure. This procedure typically concerns a number of common concepts that are considered the stages of the proposed methodology. The most important of these are the following:* Definition, description and representation of the numbers and types of entities (such as machines, people and equipment) that compose the system under study as well as their position in the operation procedure (stage, position, inputs and outputs, interconnections and process performed). This part is the initial part of the modelling procedure, as it is critical for the definition of the structure of the model. In order to transform the provided flow diagrams in diagrams of components, this part has to be defined accurately. Such information can be acquired in the initial design part of the model and is considered static data that can change only periodically when structural changes in the system take place (probably once or twice a year), as they represent decisions of high cost and complexity (rather strategic ones).