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Maintenance, shutdown and production scheduling in semiconductor robotic cells

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  • Daniel Tonke
  • Martin Grunow

Abstract

Our approach is the first to study simultaneous scheduling of preventive maintenance, shutdowns and production for robotic cells in semiconductor manufacturing. It hereby exploits the frequent periods of overcapacity in semiconductor manufacturing to reduce wear and tear. In contrast to existing approaches, our scheduling approach is able to deal with different preventive-maintenance types. We borrow the Resource Task Network representation from the process-industry domain to represent our problem and facilitate its formulation as a mathematical model. In addition, we develop efficiency-improving constraints based on the characteristics of the preventive-maintenance activities. In numerical tests based on industry data, we show that the model generates high-quality schedules even without applying the inequalities, although the optimality gap is reduced only when including inequalities. We furthermore assess the trade-off between shutdowns and batch lead times. We compare our model’s schedule quality to (i) the simple industry practice of shutting down chambers permanently to reduce wear and tear and (ii) an approach that schedules maintenance and production sequentially. The numerical tests yield the following managerial insights. First, integrating maintenance and production scheduling has substantial advantages. Second, the practice of shutting equipment down permanently diminishes scheduling flexibility and solution quality. Third, shutdowns scheduling must also consider the impact on batch waiting times.

Suggested Citation

  • Daniel Tonke & Martin Grunow, 2018. "Maintenance, shutdown and production scheduling in semiconductor robotic cells," International Journal of Production Research, Taylor & Francis Journals, vol. 56(9), pages 3306-3325, May.
  • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:9:p:3306-3325
    DOI: 10.1080/00207543.2018.1444809
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    Cited by:

    1. Mohammad Reza Komari Alaei & Mehmet Soysal & Atabak Elmi & Audrius Banaitis & Nerija Banaitiene & Reza Rostamzadeh & Shima Javanmard, 2021. "A Bender’s Algorithm of Decomposition Used for the Parallel Machine Problem of Robotic Cell," Mathematics, MDPI, vol. 9(15), pages 1-15, July.
    2. Lin, Danping & Jin, Baoping & Chang, Daofang, 2020. "A PSO approach for the integrated maintenance model," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    3. Xiufang Zhang & Tangbin Xia & Ershun Pan & Yuqing Li, 2022. "Integrated optimization on production scheduling and imperfect preventive maintenance considering multi-degradation and learning-forgetting effects," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 451-482, June.

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