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Hybrid ASP-Based Multi-objective Scheduling of Semiconductor Manufacturing Processes

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Logics in Artificial Intelligence (JELIA 2023)

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Abstract

Modern semiconductor manufacturing involves intricate production processes consisting of hundreds of operations, which can take several months from lot release to completion. The high-tech machines used in these processes are diverse, operate on individual wafers, lots, or batches in multiple stages, and necessitate product-specific setups and specialized maintenance procedures. This situation is different from traditional job-shop scheduling scenarios, which have less complex production processes and machines, and mainly focus on solving highly combinatorial but abstract scheduling problems. In this work, we address the scheduling of realistic semiconductor manufacturing processes by modeling their specific requirements using hybrid Answer Set Programming with difference logic, incorporating flexible machine processing, setup, batching and maintenance operations. Unlike existing methods that schedule semiconductor manufacturing processes locally with greedy heuristics or by independently optimizing specific machine group allocations, we examine the potentials of large-scale scheduling subject to multiple optimization objectives.

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Notes

  1. 1.

    https://github.com/prosysscience/FJSP-SMT2020.

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Acknowledgments

This work was funded by FFG project 894072 (SwarmIn) as well as KWF project 28472, cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse. We are grateful to the anonymous reviewers for their helpful comments.

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Authors and Affiliations

  1. University of Klagenfurt, Klagenfurt am Wörthersee, Austria

    Mohammed M. S. El-Kholany, Ramsha Ali & Martin Gebser

  2. Cairo University, Giza, Egypt

    Mohammed M. S. El-Kholany

  3. Graz University of Technology, Graz, Austria

    Martin Gebser

Authors
  1. Mohammed M. S. El-Kholany
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Correspondence to Martin Gebser .

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Editors and Affiliations

  1. TU Dresden, Dresden, Germany

    Sarah Gaggl

  2. Artificial Intelligence Research Institute - IIIA CSIC, Barcelona, Spain

    Maria Vanina Martinez

  3. Umeå University, Umeå, Sweden

    Magdalena Ortiz

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El-Kholany, M.M.S., Ali, R., Gebser, M. (2023). Hybrid ASP-Based Multi-objective Scheduling of Semiconductor Manufacturing Processes. In: Gaggl, S., Martinez, M.V., Ortiz, M. (eds) Logics in Artificial Intelligence. JELIA 2023. Lecture Notes in Computer Science(), vol 14281. Springer, Cham. https://doi.org/10.1007/978-3-031-43619-2_17

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