Citation
Hybridized Genetic Algorithms to Schedule Interfering Job Sets on a Single Machine. In proceedings of the 5th Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2011), 9-11 August 2011, Phoenix, Arizona, USA, pages 185-194, 2011.
Paper
Abstract
In this paper, we study a bicriteria single machine scheduling problem. The jobs belong to two disjoint sets. We consider the total weighted completion time (TWC) as the performance measure for the jobs of the first set, whereas the maximum lateness ( Lmax ) is taken as the objective for the second set. All jobs are ready at time zero. They have to be scheduled non-preemptively on the single machine. Our aim is to generate the set of all non-dominated solutions. Because of the NP-hardness of the problem, we propose heuristics based on the Non-Dominated Sorting Genetic Algorithm (NSGA)-II and random key encoding. We show by computational experiments on randomly generated problem instances that the quality of the algorithms can be improved significantly when problem specific properties are used.
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Bibtex
@INPROCEEDINGS{2011-185-194-P, author = {R. Ramacher and L. Mönch},
title = {Hybridized Genetic Algorithms to Schedule Interfering Job Sets on a Single Machine},
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 = {185--194},
note = {Paper},
abstract = {In this paper, we study a bicriteria single machine scheduling problem. The jobs belong to two disjoint sets. We consider the total weighted completion time (TWC) as the performance measure for the jobs of the first set, whereas the maximum lateness ( Lmax ) is taken as the objective for the second set. All jobs are ready at time zero. They have to be scheduled non-preemptively on the single machine. Our aim is to generate the set of all non-dominated solutions. Because of the NP-hardness of the problem, we propose heuristics based on the Non-Dominated Sorting Genetic Algorithm (NSGA)-II and random key encoding. We show by computational experiments on randomly generated problem instances that the quality of the algorithms can be improved significantly when problem specific properties are used.},
owner = {gxk},
timestamp = {2011.08.15},
webpdf = {2011-185-194-P.pdf} }