Citation
A Multi Species Evolutionary Algorithm for Tool Group Scheduling in Semiconductor Manufacturing. In proceedings of the 5th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2011), 9-11 August 2011, Phoenix, Arizona, USA, pages 459-468, 2011.
Paper
Abstract
In semiconductor manufacturing, we face many complex scheduling problems, which are difficult to tackle with conventional optimization techniques. Evolutionary algorithms are a reasonable method to approach these problems. However, their quality depends on the adequateness of the employed problem encoding and determining the best problem representation is a big challenge. Therefore we propose to use a number of different representations (species), competing with each other in an evolutionary algorithm. We developed an extensible evolutionary algorithm, which adheres to a modular design philosophy enabling customization in a multitude of ways. It supports multiple species and races, different selection strategies and further extensions like aging. A typical problem we investigate is offline scheduling for parallel cluster tools, which effectively is a combination of partitioning and sequencing of jobs with additional constraints. In this paper we try to maximize the throughput of a cluster tool group by minimizing the makespan of the scheduled jobs. The multi-species approach we employed generated promising results for this particular problem.
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Bibtex
@INPROCEEDINGS{2011-459-468-P, author = {T. Uhlig and O. Rose},
title = {A Multi Species Evolutionary Algorithm for Tool Group Scheduling in Semiconductor Manufacturing},
booktitle = {In proceedings of the 5th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2011), 9-11 August 2011, Phoenix, Arizona, USA},
year = {2011},
editor = {J. Fowler and G. Kendall and B. McCollum},
pages = {459--468},
note = {Paper},
abstract = {In semiconductor manufacturing, we face many complex scheduling problems, which are difficult to tackle with conventional optimization techniques. Evolutionary algorithms are a reasonable method to approach these problems. However, their quality depends on the adequateness of the employed problem encoding and determining the best problem representation is a big challenge. Therefore we propose to use a number of different representations (species), competing with each other in an evolutionary algorithm. We developed an extensible evolutionary algorithm, which adheres to a modular design philosophy enabling customization in a multitude of ways. It supports multiple species and races, different selection strategies and further extensions like aging. A typical problem we investigate is offline scheduling for parallel cluster tools, which effectively is a combination of partitioning and sequencing of jobs with additional constraints. In this paper we try to maximize the throughput of a cluster tool group by minimizing the makespan of the scheduled jobs. The multi-species approach we employed generated promising results for this particular problem.},
owner = {gxk},
timestamp = {2011.08.15},
webpdf = {2011-459-468-P.pdf} }