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Grouping genetic algorithms for solving single machine multiple orders per job scheduling problems

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  • Oleh Sobeyko
  • Lars Mönch

Abstract

In this paper, we discuss scheduling problems motivated by a real-world production setting found in 300-mm semiconductor wafer fabrication facilities (wafer fabs). Front opening unified pods (FOUPs) transfer wafers in 300-mm wafer fabs. Several orders can be grouped in one FOUP. We study lot and single item processing environments. The total weighted tardiness (TWT) and the weighted number of tardy orders (WNTO) objectives are considered. Mixed integer programming (MIP) formulations are presented for the scheduling problems. We prove that the researched scheduling problems are NP-hard. Grouping genetic algorithms (GGAs) are proposed to form the content of the FOUPs. We compare the performance of the GGAs with another GA from the literature available for the problem with TWT measure based on randomly generated problem instances. It turns out that the GGA outperforms the heuristic from the literature for both environments. For the WTNO measure, we assess the performance of the GGA approach using MIP formulations for small-size problem instances and a GA-based heuristic for large-size problem instances. Again, the GGA performs well with respect to solution quality and amount of computing time. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Oleh Sobeyko & Lars Mönch, 2015. "Grouping genetic algorithms for solving single machine multiple orders per job scheduling problems," Annals of Operations Research, Springer, vol. 235(1), pages 709-739, December.
    • Handle: RePEc:spr:annopr:v:235:y:2015:i:1:p:709-739:10.1007/s10479-015-1976-4
    DOI: 10.1007/s10479-015-1976-4
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    References listed on IDEAS

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    1. Koksalan Kondakci, Suna & Bekiroglu, Tuncay, 1997. "Scheduling with bicriteria: total flowtime and number of tardy jobs," International Journal of Production Economics, Elsevier, vol. 53(1), pages 91-99, November.
    2. Mason, Scott J. & Chen, Jen-Shiang, 2010. "Scheduling multiple orders per job in a single machine to minimize total completion time," European Journal of Operational Research, Elsevier, vol. 207(1), pages 70-77, November.
    3. Fehmi Tanrisever & Erhan Kutanoglu, 2008. "Forming and scheduling jobs with capacitated containers in semiconductor manufacturing: Single machine problem," Annals of Operations Research, Springer, vol. 159(1), pages 5-24, March.
    4. Laub, Jeffrey D. & Fowler, John W. & Keha, Ahmet B., 2007. "Minimizing makespan with multiple-orders-per-job in a two-machine flowshop," European Journal of Operational Research, Elsevier, vol. 182(1), pages 63-79, October.
    5. Jagadish Jampani & Scott Mason, 2008. "Column generation heuristics for multiple machine, multiple orders per job scheduling problems," Annals of Operations Research, Springer, vol. 159(1), pages 261-273, March.
    6. Vishnu Erramilli & Scott Mason, 2008. "Multiple orders per job batch scheduling with incompatible jobs," Annals of Operations Research, Springer, vol. 159(1), pages 245-260, March.
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    Cited by:

    1. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    2. Jens Rocholl & Lars Mönch & John Fowler, 2020. "Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost," Journal of Business Economics, Springer, vol. 90(9), pages 1345-1381, November.
    3. Zdeněk Hanzálek & Přemysl Šůcha, 2017. "Time symmetry of resource constrained project scheduling with general temporal constraints and take-give resources," Annals of Operations Research, Springer, vol. 248(1), pages 209-237, January.
    4. Chen-Fu Chien & Chung-Jen Kuo & Chih-Min Yu, 2020. "Tool allocation to smooth work-in-process for cycle time reduction and an empirical study," Annals of Operations Research, Springer, vol. 290(1), pages 1009-1033, July.

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