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.
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.
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.
Especially in complex manufacturing systems and uncertain conditions, sequencing operations in a machines’ queue can pose a difficult problem. To solve the problem, intelligent, adaptable, and autonomous systems have been developed using machine learning and simulation to sequence operations under uncertainty within large manufacturing systems.
This paper focuses on optimizing the production of a real-world pre-assembly facility in a high-volume and high-mix semiconductor wafer fab. In simulation software, the researchers built an in-depth, deterministic discrete-event model. With it, they tested release policies in two stages, using real production data and demands.
This paper presents a comprehensive production scheduling approach that combines optimization and simulation to cope with parameter uncertainty.
The approach allows for identifying and including demand fluctuations and scrap rates. Furthermore, the researchers adapt seven optimization algorithms for two-stage hybrid flow shops with unrelated machines, machine qualifications, and skipping stages with the objective to minimize the makespan.
The combination of methods is validated on a real production case of the automobile industry.
The overhead crane scheduling problem has been of interest to many researchers. While most approaches are optimization-based or use a combination of simulation and optimization, this research suggests a combination of dynamic simulation and reinforcement learning-based AI as a solution.
The goal of this steel plant simulation project was to minimize the crane waiting time at the LD converters by creating a better crane schedule.
This case study focuses on the simulation of a soon-to-be-implemented automation system within a Walmart Canada warehouse. This new system's aim is more efficient warehouse operations. Many stock-keeping units (SKUs) cannot be sent to retail stores in full case quantities. They are slow movers and would require individual stores to carry excessive inventory.
Breakpack is the process of breaking cases down to individual eaches (pieces) and combining them into mixed SKU cartons. Automating breakpack offers significant labor and quality savings, that are important to ensure efficient warehouse operations, but also a high degree of complexity.
Due to various production and market factors, flexibility is a key point in semiconductor manufacturing supply chain design. However, the increased complexity associated with this flexibility must be effectively managed to leverage the benefits that flexibility provides. The product structure is one of the main factors for enabling the desired result. Product structure representations in the supply chain design include linear, tree, and network. In this paper, the researchers explain the problem by a real case merger where risk and opportunities based on the choice of product structure representation in the supply chain design were relevant and no final solution initially was determined.
Multi-resolution simulation models of a manufacturing system, such as a virtual factory, coupled with simulation-based analytics offer exciting opportunities to manufacturers to exploit the increasing availability of data from their corresponding real factory at different hierarchical levels. A virtual factory simulation model can be maintained as a live representation of the real factory and used to highly accelerate learning from data using simulation-based analytics applications.
This paper proposes a shared infrastructure for a virtual factory simulation-based analytics that can be employed by small and medium enterprises.