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.
The Regional Biorefinery Impact Assessment (REBIA) simulation tool allows easy impact assessment for waste stream sources and biorefinery locations. REBIA is based on a supply chain model, where waste is transported via the road network from its various sources to the biorefinery.
A proof-of-concept model demonstrates how the development of a circular economy can impact energy use and the environment. The aim is to consider several aspects that contribute to a thriving community: energy requirements, jobs, tourism, supply lines, and the impact of waste on water resources and the land (landfill).
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.
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.
This study uses agent-based modeling as a proof of concept tool to investigate the applicability of the green performance bond framework to provide insights into the potential benefits of implementing it within the construction industry. The research also evaluates its feasibility and effectiveness in discouraging opportunistic bidding behaviors.
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.
Pallets are returnable transport items and of great importance for supply chains. They ensure efficient storage, transport, and handling processes. The pallet cycle, however, is associated with a substantial effort. In addition to administrative costs, extra trips and detours must often be taken by forwarders to retrieve pallets or buy new pallets. In this paper, a fictitious cross-actor pallet exchange platform is analyzed by building a supply chain model.
Factors including hospital space layout, patient behavior, patient flow, and medical procedures interact and relate to each other, and ultimately affect efficiency and performance of healthcare facilities. And hospital layout planning can’t ignore such interdependencies.
This research integrates discrete event simulation (DES) and agent-based simulation (ABS) to help managers examine, plan, and compare different spatial design schemes through the modeling of patient behavior, patient flow, and the establishment of evaluation indexes.
In Ireland, cheese is one of the most exported products. Due to Brexit, it will be most impacted by delays at ports caused by the potential reintroduction of customs or border controls. To predict forthcoming changes and analyze their impact on the cheese supply chain in Ireland, a simulation model was built. To develop the model, the research team used a combination of the agent-based and discrete event modeling approaches along with geographic information system (GIS) included in AnyLogic simulation software.