Konferenzbeiträge

Generic Semiconductor Manufacturing Simulation Model


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.

Applying Simheuristics for Safety Stock and Planned Lead Time Optimization in a Rolling Horizon MRP System under Uncertainty


Material requirements planning (MRP) is one of the main production planning approaches implemented in enterprise resource planning systems, and one that is broadly applied in practice. In this paper, a multi-stage and multi-item production system is simulated by considering random customers’ demands and other sources of uncertainty.

Simulating Backfill Operations for Underground Mining


This article focuses on the simulation model that was developed for Sibanye-Stillwater’s underground platinum mining operations in Nye, MT. The model was designed to help the mining company understand how bottlenecks move through their operations, to help identify which resources are constraining underground mining production increases, and to understand where capital investments are needed in backfill operations.

Using Simulation to Analyze the Predictive Maintenance Technique and its Optimization Potential


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.

Building a Predictive Analytics Simulation Model of a Semiconductor Manufacturing Facility


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.

Simulation-Based Scheduling and Planning Approach to Job-Shop Production 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.

Optimize Hybrid Flow Shop Production Scheduling under Uncertainty


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.

Crane Scheduling at Steel Manufacturing Plant Using Simulation Software and AI


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.