Complex Job Shop Simulation “CoJoSim”—A Reference Model for Simulating Semiconductor Manufacturing
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Published:2023-03-12
Issue:6
Volume:13
Page:3615
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Bauer Dennis12ORCID, Umgelter Daniel12ORCID, Schlereth Andreas1ORCID, Bauernhansl Thomas13, Sauer Alexander12
Affiliation:
1. Fraunhofer Institute for Manufacturing Engineering and Automation IPA, 70569 Stuttgart, Germany 2. Institute for Energy Efficiency in Production EEP, University of Stuttgart, 70569 Stuttgart, Germany 3. Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, 70569 Stuttgart, Germany
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.
Funder
Austria, Germany, Italy, France, Portugal and—Electronic Component Systems for European Leadership Joint Undertaking
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference58 articles.
1. Mack, O., Khare, A., Krämer, A., and Burgartz, T. (2016). Managing in a VUCA World, Springer. 2. Bauernhansl, T., Hörcher, G., Bressner, M., and Röhm, M. (2018). MANUFUTURE-DE: Identification of Priority Research Topics for the Sustainable Development of European Research Programs for the Manufacturing Industry until 2030, Fraunhofer IPA. 3. Managing Complexity in Supply Chains: A Discussion of Current Approaches on the Example of the Semiconductor Industry;Aelker;Procedia CIRP,2013 4. Mönch, L., Fowler, J.W., and Mason, S.J. (2013). Production Planning and Control for Semiconductor Wafer Fabrication Facilities: Modeling, Analysis, and Systems, Springer. 5. A survey of semiconductor supply chain models part I: Semiconductor supply chains, strategic network design, and supply chain simulation;Uzsoy;Int. J. Prod. Res.,2018
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