TY - JOUR
T1 - Genetic algorithm based scheduling of parallel batch machines with incompatible job families to minimize total weighted tardiness
AU - Balasubramanian, Hari
AU - Mönch, Lars
AU - Fowler, John
AU - Pfund, Michele
N1 - Funding Information:
This research was supported by the Factory Operations Research Center which is jointly funded by International SEMATECH and the Semiconductor Research Corporation (SRC) under contract 2001-NJ-880. Parts of this research were carried out when the second author was visiting the Modeling and Analysis of Semiconductor Manufacturing (MASM) Laboratory at Arizona State University, Tempe.
PY - 2004/4/15
Y1 - 2004/4/15
N2 - This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication, where the machines can be modelled as parallel batch processors. We attempt to minimize total weighted tardiness on parallel batch machines with incompatible job families. Given that the problem is NP-hard, we propose two different versions of a genetic algorithm (GA), each consisting of three different phases. The first version forms fixed batches, then assigns batches to the machines using a GA, and finally sequences the batches on individual machines. The second version assigns jobs to machines using a GA, then forms batches on each machine for the jobs assigned to it, and finally sequences these batches. Heuristics are used for the batching phase and the sequencing phase. For both these versions an additional fourth phase can be included wherein the sequenced batches are modified using pairwise swapping techniques. Using stochastically generated test data we show that algorithms of the first version of the GA outperform (1) traditional dispatching rules with respect to solution quality and (2) the algorithms of the second version with respect to both solution quality and computation time.
AB - This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication, where the machines can be modelled as parallel batch processors. We attempt to minimize total weighted tardiness on parallel batch machines with incompatible job families. Given that the problem is NP-hard, we propose two different versions of a genetic algorithm (GA), each consisting of three different phases. The first version forms fixed batches, then assigns batches to the machines using a GA, and finally sequences the batches on individual machines. The second version assigns jobs to machines using a GA, then forms batches on each machine for the jobs assigned to it, and finally sequences these batches. Heuristics are used for the batching phase and the sequencing phase. For both these versions an additional fourth phase can be included wherein the sequenced batches are modified using pairwise swapping techniques. Using stochastically generated test data we show that algorithms of the first version of the GA outperform (1) traditional dispatching rules with respect to solution quality and (2) the algorithms of the second version with respect to both solution quality and computation time.
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U2 - 10.1080/00207540310001636994
DO - 10.1080/00207540310001636994
M3 - Article
AN - SCOPUS:1542685255
VL - 42
SP - 1621
EP - 1638
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 8
ER -