Explore how a training facility at Auburn University is teaching students and professionals lean manufacturing by assembling LEGO® cars. Through a multi-phase plan, the facility is evolving into a hub for digital manufacturing and Industry 4.0 technologies, with a focus on small and medium manufacturers (SMMs). The current phase involves creating an agent-based digital twin model to simulate and optimize assembly line processes.
Efficient material replenishment in assembly plants can be optimized using autonomous mobile robots. This research analyzes the Tiger Motors assembly line at Auburn University, where a Stretch RE1 robot is integrated into the production process. A multimethod simulation model combining discrete-event and agent-based simulation modeling in AnyLogic evaluates different replenishment strategies. It identifies the most effective approach based on payload capacity, travel optimization, and real-time demand response.
Efficient production scheduling is crucial for maximizing resource utilization in eco-industrial parks, where factories share energy and materials. However, balancing energy distribution and job scheduling is challenging due to fluctuating power availability, production constraints, and inter-factory dependencies. This study uses a constraint programming model in AnyLogic combining agent-based and discrete-event simulation modeling to optimize production in an industrial park.
As the semiconductor market booms, set to hit 1 trillion USD by 2030, the pursuit of sustainable semiconductor manufacturing is critical. This article examines Infineon Technologies' efforts to reduce energy consumption in semiconductor manufacturing and describes how improvements can enhance sustainability in this industry. Infineon uses simulation models to pinpoint energy-saving opportunities, making a strong case for eco-friendly advancements in the tech industry.
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 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.
Through simulation modeling, this research highlighted the interactions of key system parameters in a disruption phase under different scenarios. A multi-period, multi-echelon serial supply chain was studied with agent-based and discrete-event simulation.
The semiconductor industry is facing pressure to reduce its extensive energy consumption, which requires transparency on the relationship between energy efficiency and original planning objectives. This paper aims to develop an extension to the existing Operating Curve concept by investigating the effect of utilization on energy efficiency. It uses the results of discrete-event simulation on a fab level to verify the novel concept.
As widely reported in the literature, the leather luxury accessories industry is characterized by a highly fragmented supply chain. Frequent changes in the production mix had to be managed, often requiring the re-optimization or even re-design of production flows. The objective of this paper was to propose a data-driven simulation model for production balancing and optimization in this sector.