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.
Using simulation models for manufacturing facilities is a common approach for planning, optimizing, and testing different machine configurations and positioning before the actual construction. This paper presented a proof of concept for gradually migrating a master simulation model for shop floor layouts of machines into a product line of different simulation models to explore and find suitable solutions.
This paper presented a concept for simulation-based optimization of the system design of modular production systems using a classical Genetic Algorithm (GA) and the NSGA-II algorithm, which supported a good activity assignment to the individual manufacturing cells.
The job-shop scheduling problem was considered with sequence-dependent setup times and preventive maintenance constraints. A hybrid model combining discrete-event simulation and an optimization algorithm in Python was applied to simulate the production process and solve the job-shop problem.
Co-design (concurrent engineering) is an important prerequisite for finding the best design solution for both future aircraft and the corresponding industrial systems. This paper discusses the use of simulation in the process of co-defining the optimal industrial system configuration for a future aircraft.
Simulation is one of the most used approaches to analyze semiconductor manufacturing systems. However, most simulation models are designed for single-use applications and study a limited set of problems that are not reusable afterwards. This paper proposes a generic, data-driven simulation model to evaluate and analyze a wide range of problems arising in modern semiconductor manufacturing systems.