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.
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