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Authors: Helga Ingimundardottir and Thomas Philip Runarsson

Affiliation: University of Iceland, Iceland

Keyword(s): Job Shop Scheduling, Composite Dispatching Rules, Evolutionary Search.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolution Strategies ; Evolutionary Computing ; Soft Computing

Abstract: A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done on an ad-hoc basis, requiring expert knowledge from heuristics designer, or extensive exploration of suitable combinations of heuristics. The approach here, is to automate that selection, by translating dispatching rules into measurable features and optimising what their contribution should be via evolutionary search. The framework is straight forward and easy to implement and shows promising results. Various data distributions are investigated, for both job shop and flow shop problems, as is scalability for higher dimensions. Moreover, the study shows that the choice of objective function for evolutionary search is worth investigating. Since the optimisation is based on minimising the expected mean of the fitness function over a large set of problem instances, which can vary w ithin. Then normalising the objective function can stabilise the optimisation process away from local minima. (More)

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Paper citation in several formats:
Ingimundardottir, H. and Runarsson, T. (2014). Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling. In Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2014) - ECTA; ISBN 978-989-758-052-9, SciTePress, pages 59-67. DOI: 10.5220/0005077200590067

@conference{ecta14,
author={Helga Ingimundardottir. and Thomas Philip Runarsson.},
title={Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2014) - ECTA},
year={2014},
pages={59-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005077200590067},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2014) - ECTA
TI - Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling
SN - 978-989-758-052-9
AU - Ingimundardottir, H.
AU - Runarsson, T.
PY - 2014
SP - 59
EP - 67
DO - 10.5220/0005077200590067
PB - SciTePress