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

Oleh Sobeyko and Lars Mönch ()

Annals of Operations Research, 2015, vol. 235, issue 1, 709-739

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

Keywords: Scheduling; Multiple orders per job; Grouping genetic algorithm; Semiconductor manufacturing; Computational experiments (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-015-1976-4

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