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This book describes and outlines the theoretical foundations of system simulation in teaching, and as a practical contribution to teaching-and-learning models. It presents various methodologies used in teaching, the goal being to solve real-life problems by creating simulation models and probability distributions that allow correlations to be drawn between a real model and a simulated model. Moreover, the book demonstrates the role of simulation in decision-making processes connected to teaching and learning.

Problem-Based Learning: A Didactic Strategy in the Teaching of System Simulation

The System Simulation and their Learning Processes.- Process Sampling.- Pseudo-random Numbers and Congruential Methods.- Random Variable Generation Methods.- Monte Carlo Simulation Method.- Case Study: Logistical Behavior in the use of Urban Transport Using the Monte Carlo Simulation Method.- Case Study: Project-Based Learning to Evaluate Probability Distributions in Medical Area.- Case Study: Probabilistic Estimates in the Application of Inventory Models for Perishable Products in SMEs.