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International Conference on Artificial Intelligence and Symbolic Computation

AISC 1998: Artificial Intelligence and Symbolic Computation pp 120–131Cite as

An evolutionary algorithm for welding task sequence ordering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1476))

Abstract

In this paper, we present some of the results of an ongoing research project, which aims at investigating the use of the evolutionary computation paradigm for real world problem solving in an industrial environment. One of the problems targeted in the investigation is that of job sequence optimization for welding robots operating in a shipyard. This is an NP-hard combinatorial optimization problem with constraints. To solve the problem, we propose a hybrid genetic algorithm incorporating domain-specific knowledge. We demonstrate how the method is successful in solving the job sequencing problem. The effectiveness and usefulness of the algorithm is further exemplified by the fact, that it has been implemented in the RoboCopp application program, which is currently used as the task sequence scheduler in a commercially available robot programming environment.

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Author information

Authors and Affiliations

  1. AMROSE A/S, Forskerparken 10, DK-5230, Odense M, Denmark

    Martin Damsbo

  2. The Maersk Mc-Kinney Moller Institute for Production Technology, Odense University, Campusvej 55, DK-5230, Odense M, Denmark

    Peder Thusgaard Ruhoff

Authors
  1. Martin Damsbo
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  2. Peder Thusgaard Ruhoff
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Jacques Calmet Jan Plaza

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© 1998 Springer-Verlag Berlin Heidelberg

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Damsbo, M., Ruhoff, P.T. (1998). An evolutionary algorithm for welding task sequence ordering. In: Calmet, J., Plaza, J. (eds) Artificial Intelligence and Symbolic Computation. AISC 1998. Lecture Notes in Computer Science, vol 1476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0055907

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  • DOI: https://doi.org/10.1007/BFb0055907

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64960-1

  • Online ISBN: 978-3-540-49816-2

  • eBook Packages: Springer Book Archive

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