Abstract
The pluralistic approach in today's world needs combining multiple methods, whether hard or soft, into a multi-methodology intervention. The methodologies can be combined, sometimes from several different paradigms, including hard and soft, in the form of a multi-methodology so that the hard paradigms are positivistic and see the organizational environment as objective, while the nature of soft paradigms is interpretive. In this chapter, the combination of methodologies has been examined using soft systems methodologies (SSM) and simulation methodologies including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM). Also, using the ontological, epistemological, and methodological assumptions underlying the respective paradigms, the difference between SD, ABM, SSM; a synthesis of SSM and SD generally known as soft system dynamics methodology (SSDM); and a promising integration of SSM and ABM referred to as soft systems agent-based methodology (SSABM) have been proven.
TopDiscrete Event Simulation (Des)
Due to the dynamic, stochastic, discrete nature, DES simulation model accounts for randomness and time, as a discrete variable, has points where the system changes are displayed. DES models, which provide a more accurate representation of a system than other simulation techniques, are commonly used to analyze systems at the strategic level and are suitable for “what” scenarios in analysis to improve system capacity, using resources or queuing time.
Entities, events, activities, and attributes are key aspects of DES modeling that respectively represent objects flowing in the system, indigenous and exogenous events of the system responsible for changing the system that trigger the onset of activity, and attributes attributed to the entity. Event calendar contains status information of all entities and event-related information is another key concept of DES modeling. Also, tracking the simulation time and progress in discrete time steps of the next event in the calendar is done by the simulation clock as another component; when there are not enough resources in the model to process the activity, the entity queue emerges. Random draws of activities from a separate probability distribution is performed using a random generator that is responsible for the randomness of the model (Banks et al., 2010).