Análise elementar da complexidade de modelos de simulação discretos e híbridos

Abstract

Under the influence of technological development, real systems are experiencing a significant increase in size and complexity. Faced with a context of technological advances, simulation maintains its scientific relevance, supporting business decision-making. Through simulation, real systems, and their idiosyncrasies, are analyzed and improved. During a simulation project, the specialist needs to make a series of decisions, which includes defining the model's level of detail. Furthermore, in the computational model development phase, the specialist also needs to decide on which simulation approach to use. In the midst of this specialist's decisions, a dilemma emerges: real systems are progressively becoming larger and more complex as a result of technological progress; even with technological advances, the scientific literature states that a computational model is an abstraction of reality and it should be as simple as possible. In the context of this dilemma, the present thesis has the general objective of deepening discussions on the introduction of a greater level of detail in computational models, considering the Discrete Event Simulation (DES) and Hybrid Simulation (HS) approaches, id est, DES approach combined with Agent-Based Simulation (SBA) approach. The need to deepen these discussions generated the iDAV method, a method used to measure computational models. With the application of the iDAV method, it was found that discrete models are simpler to be developed when the level of detail is lower. On the other hand, when the scope and level of detail are increased, hybrid models are more suitable.