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.

Using Simulation to Analyze the Predictive Maintenance Technique and its Optimization Potential

The Predictive Maintenance technique offers a possibility to improve productivity in semiconductor manufacturing. Current research on Predictive Maintenance mainly focuses on its technical implementation. By applying discrete-event simulation, the research team provide results on how maintenance strategies can help optimize machine operations, and how the technique contributes to an overall improvement of productivity in wafer fabrication.

Deep Reinforcement Learning Approach for Inventory Policy Tested in Simulation Environment

In this work, the researchers undertake a root-cause enabling Vendor Managed Inventory performance measurement approach to assign responsibilities for poor performance. Additionally, the work proposes a solution methodology based on reinforcement learning for determining optimal replenishment policy in a VMI setting. Using a simulation model as a training environment, different demand scenarios are generated based on real data from Infineon Technologies AG and compared based on key performance indicators.

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.

Simulation-Based Scheduling and Planning Approach to Job-Shop Production System

This paper proposes a simulation-based decentralized planning and scheduling approach to improve the performances of a job-shop production system, compliant with a semi-heterarchical Industry 4.0 architecture. To this extent, to face the increasing complexity of such a scenario, a parametric simulation model able to represent a wide number of job-shop systems is introduced.