Agent-Based Simulation to Predict Occupants’ Physical-Distancing Behaviors in Educational Buildings

Physical distancing is recommended as the most efficient strategy for defending individuals from long-term global pandemics. It lowers the risk of community spreading, especially for indoor spaces. This paper aims to model physical-distancing behaviors in an educational facility, using agent-based simulation, and evaluate the impact of measures (e.g., controlling classroom capacity, breaktime scheduling) on physical distance violation risks.

Pedestrian Flow Simulation to Prove Effectiveness of Subway Barriers

In the last few years, there has been an increase in accidents involving pedestrians in the Mexico City subway. A proposed solution is to install physical barriers between platforms and tracks. The research team built an agent-based pedestrian flow simulation model to prove the effectiveness of such barriers.

Training Reinforcement Learning Policy in AnyLogic Simulation Environment Using Pathmind

In this paper, the researchers study the operations of an imaginary coffee shop with a focus on the barista’s actions. They also show how the sequence of actions affects the overall performance of the coffee shop by using reinforcement learning and simulation as its policy training environment. This model acts as a guiding example that shows the ease of applying RL in AnyLogic models using the Pathmind Library.

Modeling the Mechanisms of Friendship Network Formation

The study of friendship formation is fundamental to the study of human beings. In this paper, the research team presents an agent-based model of friendship networks grounded in the existing empirical research literature on friendship formation. The goal is to better understand what mechanisms might be influential in the formation of friendships as well as how such modeling might inform (and potentially advance) our understanding of existing empirical work.

Building a Predictive Analytics Simulation Model of a Semiconductor Manufacturing Facility

The purpose of the article is to create a predictive analytics simulation model to help managers anticipate manufacturing issues. It integrates specifically the involvement of human resources in the manufacturing systems. The predictive analytics simulation model also includes the main existing interactions between the operators and the manufacturing system.