Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing

Abstract

The manufacturing industry is facing increasing volatility, uncertainty, complexity, and ambiguity, while still requiring high delivery reliability to meet customer demands. This is especially challenging for complex job shops in the semiconductor industry, where the manufacturing process is highly intricate, making it difficult to predict the consequences of changes. Although simulation has proven to be an effective tool for optimizing manufacturing processes, reference data sets and models often produce disparate and incomparable results. CoJoSim is introduced in this article as a reference model for semiconductor manufacturing, along with an associated reference implementation that accelerates the implementation and application of the reference model. CoJoSim can serve as a testbed and gold standard for other implementations. Using CoJoSim, different dispatching rules are evaluated to demonstrate an improvement of almost 15 percentage points in adherence to delivery dates compared to the reference. Findings emphasize the importance of optimizing setup time, particularly in products with high variance, as it significantly impacts adherence to delivery dates and throughput. Moving forward, future applications of CoJoSim will evaluate additional dispatching rules and use cases. Combining CoJoSim with dispatching methods that integrate manufacturing and supply networks to optimize production planning and control through reinforcement-learning-based agents is also planned. In conclusion, CoJoSim provides a reliable and effective tool for optimizing semiconductor manufacturing and can serve as a benchmark for future implementations.

Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System

“…It is of interest to investigate whether there exist combinations of α and ∆t for which the ∑(t 3 o ) remains unchanged for ∑(t 3 n ), in order to analyze the change of these solutions for the other summation terms. Considering the position of the equations above, it is possible to write: Equation (7) highlights the relationship between ∆t and α, providing a means to explore various scenarios where the redistribution of processing time does not impact the cubic summation of processing times. By analyzing these combinations, further insights can be gained into the behavior of the system under different redistributions and their effects on other performance metrics.…”

“…The ability to supply more individualized market demand in a short period of time and at a lower cost is a vital premise for industrialized countries' competitive recovery against emerging countries with lower technological development and lower social and labor expenses [1][2][3]. The Fourth Industrial Revolution (or Industry 4.0) was born in response to this challenge, with the goal of integrating cutting-edge information technologies, artificial intelligence techniques, and distributed production control approaches [4][5][6][7][8][9]. Said differently, one of Industry 4.0's ultimate objectives is to resolve the long-standing conflict between the uniqueness of on-demand output and the cost savings attained through economies of scale.…”