The paper discusses how process mining can be used to gain insights and automatically regulate processes in real time. It highlights the need for thorough testing to avoid side effects and proposes using discrete-event simulation in production and logistics to mitigate risks.
Creating discrete-event simulation models for adaptable material flow systems is challenging due to the need for various structural variants. This paper unveils an innovative method to automate this process, illustrated by a case study, which makes it easier and more efficient.
This article presents a discussion of manufacturing process optimization in the ready-made garment industry through making the management of production lines more effective using AnyLogic software. It shows how simulation of various scenarios can pinpoint effective strategies for maintaining efficiency despite changes in market demand.
The study explores the critical need to address vulnerabilities within the semiconductor supply chain, given its global network and production timelines. It showcases the application of system dynamics simulation through AnyLogic software. The researchers assess the impacts of external disruptions and observe the change in customer orders, revenue, and distribution center stock in different operational strategies for managing supply chain disruptions.
The semiconductor manufacturing process is complex and requires smart planning to achieve production efficiency. This semiconductor research paper examines how prioritizing certain batches affects the time it takes to process others. Using a simulation model, the researchers tested different scenarios to see what worked best. The results obtained will help improve the management of production lines.
The research paper on e-commerce addresses urban logistics issues worsened by the COVID-19 pandemic. The scholars used system dynamics simulation modeling with the ε-constraint method to design a parcel locker delivery network and forecast demand. The model suggested the lockers’ locations for improved delivery results and helped to develop a strategy for minimizing environmental impact.
This research introduces a cloud-based hybrid simulation model that combines discrete-event simulation (DES) and agent-based modeling (ABM) to enhance Amazon warehouse yard operations, which are crucial for efficient logistics. By leveraging cloud technology for scalability and real-time data integration, the model dynamically simulates sequential processes and the interactions of autonomous agents, such as trucks and yard staff.
Despite advances in clinical care for the coronavirus (COVID-19) pandemic, population-wide interventions were vital to effectively manage the pandemic due to its rapid spread and the emergence of different variants. One of the most important interventions to control the spread of the disease is vaccination. In this study, an extended Susceptible-Infected Healed (SIR) model based on system dynamics was designed, considering the factors affecting the rate of spread of the COVID-19 pandemic.
The behavior of passengers in urban railway stations (i.e., metro stations) is dependent on environmental, cultural, and temporal factors. In this research, escalator infrastructures were studied to better understand the relationship between different conditions and passenger behaviors through a method based on video cameras, passenger detection techniques, and a simulation framework.
Cross-docking is a warehousing method that allows goods to move quickly from inbound suppliers directly to outbound customers, minimizing storage time. This study focuses on developing a real-time multi-agent truck scheduling model to optimize the process of cross-docking in warehouses, aiming for quick and efficient synchronization of incoming and outgoing freight.