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After articulating the problem, the next step in a simulation study is to define the conceptual model, derived from the modellers’ mental model of the system, which is then transferred into the simulation software. In SD this stage is also referred to as formulation of the dynamic hypothesis (Sterman, 2000). During this stage, based on the modelling objectives, the boundaries of the system are set by including, inputs, outputs, contents, assumptions and simplifications of the model. The aspects pertaining to conceptual modelling and which come up in the comparison of DES and SD are: diagramming methods, system representation, representation of people and feedback effects. These are discussed in the following paragraphs.

2.6.1 Diagramming

Some diagramming methods used to define SD conceptual models are: model boundary charts, subsystem diagrams, causal loop diagrams, stock and flow diagrams and policy structure diagrams (Sterman, 2000). However, causal loop diagrams or influence diagrams are most often used in practice. Conceptual

diagrams are used in order to understand the feedback structure of the system. These diagrams are used to understand the broad system structure and are therefore, kept intentionally simple (Pidd, 2003). These are also called qualitative models

(Wolstenholme, 1990), which can at times be adequate to understand the problem situation and thus a further computer model might not be necessary (Brailsford and Hilton, 2001).

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With respect to DES modelling, it is suggested that there are no set diagramming methods for representing models (Morecroft and Robinson, 2005). They vary from activity cycle diagrams, process mapping/process flow diagrams, logic flow

diagrams, to Petri nets, unified modelling language (UML), digraphs, object models, event graphs, etc. (Robinson, 2004; Onggo, 2007; Onggo, et al., 2008). The most frequently used diagramming methods are process flow diagrams and activity cycle diagrams4. Creating a conceptual model is considered beneficial in DES modelling in order to keep the model focused on project objectives and to ensure that the model achieves its requirements (Robinson, 2004).

Mak (1993), in her doctoral thesis, investigated the conversion of DES activity cycle diagrams into SD stock and flow diagrams. She developed a set of conversion guidelines, which were incorporated in the prototype automated conversion

software she developed. DES process flow diagrams, which could be considered as more close to stock and flow diagrams were not included in the study. Mak pointed out that SD modelling structures are more flexible than DES Activity Cycle

Diagram modelling. In a SD casual loop diagram, one can add as many auxiliary variables and as many information links as necessary in order to represent a situation. While in a DES activity cycle diagram only alternating activities and queues are allowed. However, at a later point, Mak (1993) comments on the flexibility of DES modelling, which allows the modeller to manipulate the events

4 _{The activity cycle diagram describes the logic of the simulation model and shows the life cycle of}

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and hence its flexibility in representing different components and activities in the model.

2.6.2 Feedback effects

In SD, models are viewed as closed systems, where the outputs have effect on the input and are represented by “a series of stocks and flows” (Brailsford and Hilton, 2001). The system’s behaviour is determined by the internal structure of the system, the causal relationships of endogenous variables incorporated into feedback loops (Sweetser, 1999; Morecroft and Robinson, 2005). In SD modelling, the focus is on the feedback processes affecting the changes to the outputs of interest (Taylor and Lane, 1998). Therefore, feedback is an important part in SD modelling. On the other hand, in DES, systems are viewed as “networks of queues and activities” (Brailsford and Hilton, 2001). It is generally claimed that DES follows an open loop structure and feedback is not modelled (Coyle, 1985). It has been argued, however, that feedback is involved in DES models (Sweetser, 1999; Lane, 2000; Morecroft and Robinson, 2005). Robinson (2004, pp.7) explains how feedback effects are present in a DES model, taking a simple example of a Kanban system, where a machine feeds a buffer. The rate at which the machine works affects the number of parts in the buffer, which in turn affects the speed at which the machine works. However, all these effects are hidden behind the computations of the simulation software and are not specifically considered by the modeller or the user. Hence, even though

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modellers are less interested in the events that cause the changes (Sweetser, 1999; Lane, 2000; Morecroft and Robinson, 2005).

2.6.3 System representation

With respect to the representation of systems, it is generally accepted that DES takes an analytic view, whereas SD takes a holistic view of a system’s performance. It is believed that SD tends to represent abstract and general systems, while DES can evaluate a variety of issues at a low level of detail (Baines et al., 1998) and so models tend to have a narrower focus (Sweetser, 1999). These beliefs can be explained by the respective philosophy taken during modelling by each simulation approach.

More specifically, SD takes a systems’ thinking perspective. The system is seen as a collection of parts and their underlying interrelationships (Bellinger, 2004). SD focuses on the emerging system behaviour over time, by exploring the dynamic implications of the underlying structure of the system. Furthermore, due to the use of systems’ thinking in conceptualising the model and the ability to include the interrelationships between various factors, SD is considered more suitable for the representation of systems with a wider focus. Abstract models can be developed with the use of approximations (one such example is the use of average values) and subsequently accurate models are not required (Baines et al., 1998). This approach typifies holistic thinking.

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In DES however, a reductionist approach is taken (Han et al., 2005), where system understanding is achieved in terms of its components. DES models break a system down into its constituent parts (Lane, 2000). DES is more oriented to representing distinct objects/people, scheduled activities, queues and decision rules (Brailsford and Hilton, 2001) in the system and the associated interdependencies. Efforts in conceptual modelling do not focus on identifying the interrelationships between the parts of the system. It is hence suggested that DES is more suitable in representing detailed and well-defined processes (Baines et al., 1998). This approach typifies an analytic way of thinking.

2.6.4 People

In SD models, the entities are ‘indistinguishable’ (Borshchev and Filippov, 2004) and the aggregate behaviour of the system population is examined. The SD

approach is particularly preferred in the case of models with a very large population (Brailsford, et al., 2004). On the contrary, in DES the entities are distinctly

represented and their behaviour in the system is individually modelled. The

characteristics attributed to the entities determine their progress in the system from the time they enter until they exit. The history of each entity in the model can be observed and state changes recorded. As a result, Pidd (2004) suggests that DES models are more appropriate when the tracking of individual entities is important.

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