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Car sequencing with constraint-based ACO
conference contribution
posted on 2011-08-24, 00:00 authored by Dhananjay ThiruvadyDhananjay Thiruvady, B Meyer, A T ErnstHybrid methods for solving combinatorial optimization problems have become increasingly popular recently. The present paper is concerned with hybrids of ant colony optimization and constraint programming which are typically useful for problems with hard constraints. However, the original algorithm suffered from large CPU time requirements. It was shown that this integration can be made efficient via a further hybridization with beam search resulting in CP-Beam-ACO. The original work suggested this in the context of job scheduling. We show here that this algorithm type is also effective on another problem class, namely car sequencing. We consider an optimization version, where we aim to optimize the utilization rates across the sequence. Car sequencing is a notoriously difficult problem, because it is difficult to obtain good bounds via relaxations. We show that stochastic sampling provides superior results to well known lower bounds for this problem when combined with CP-Beam-ACO. Copyright 2011 ACM.
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Event
Genetic and Evolutionary Computation. Conference (13th : 2011 : Dublin, Ireland)Pagination
163 - 170Publisher
ACMLocation
Dublin, IrelandPlace of publication
Washington, D.C.Publisher DOI
Start date
2011-07-12End date
2011-07-16ISBN-13
9781450305570Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
GECCO 2011 : Genetic and Evolutionary Computation Conference : July 12-16, 2011, Dublin, Ireland.Usage metrics
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