Modeling and simulation are a powerful and effective problem-solving methodology to study how complex real-world systems behave over time. In the literature, many authors have indicated that discrete event simulation, agent-based simulation, and system dynamics are the primary and most important simulation techniques to aid industrial engineers in making decisions. Given this context, this paper is expected to be an introductory guide, especially for novice simulation modelers willing to work with hybrid simulation, by providing knowledge and insight about the primary simulation methods in industrial engineering. This paper is expected to support the decision about which simulation technique best suits the system being studied. For that, a systematic literature review was conducted based on pre-defined search criteria. After applying the filters and including some relevant papers published in the field, a total of 145 papers were selected. Some of the analysis performed in this study include, for each simulation method, the number of publications over the years and a list of the top 10 sources, countries, and authors according to the number of publications. Besides that, a brief history is provided and the definition of the three primary techniques is discussed, as well as the main characteristics of each technique, such as modeling steps, elements, conceptual modeling tools used, software, inputs and outputs, programming languages, advantages, disadvantages, and application areas. Simulation modelers can use this paper as a quick reference to the primary simulation techniques in order to identify the best tool for a specific simulation project in the field of industrial engineering and related areas.
and background The modeling and simulation (M&S) field includes the methods, tools, and techniques used to represent, experiment, and study complex systems. The M&S tools and techniques have advanced in the past decades and have been increasingly applied in more challenging areas (Tuncer Ören, 2010). Models are simplified abstractions to represent a system for some specific goal and are used to test theories and to explore their implications and contradictions (Balci, 2001, 2003). Simulation is one particular approach to study models or to experiment with a model based on numerous goals (Balci, 2003; White & Ingalls, 2015). Simulation models are computer representations of how the real world system operates at some level of aggregation. Modeling and simulation are frequently more useful to promote knowledge and valuable understanding about the system and the problem structure than to provide accurate predictions and exact answers (Eldabi et al., 1999; Winz et al., 2009). The definition of modeling and simulation can be found in Maria (1997); White and Ingalls (2009, 2015, 2016). Simulation is frequently the most time-effective and cost-effective, and every so often the only means of detecting causal effects, stipulating critical parameter estimates and clarifying how processes develop over time (Garson, 2009). Simulation allows people to analyze systems optimization prior to implementation. In general, simulation is a more suitable methodology to investigate complex problems, especially when the problem cannot be formulated in mathematical terms (Barton et al., 2013; Huanhuan et al., 2013).