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GA/TS: A hybrid approach for job shop scheduling in a production system

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Progress in Artificial Intelligence (EPIA 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 990))

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Abstract

This paper outlines a new efficient approach to solve combinatorial optimization problems making use of a hybrid search method. The approach integrates genetic algorithms (GA) and tabu search (TS) techniques to be incorporated into a generic architecture for a knowledge-based manufacturing system.

In this paper, a new method, called GA/TS, has been developed. The proposed method pursues a hybrid schedule generation strategy wherein it effectively combines knowledge acquired via genetics-based induction with tabu search methodology. We have used this hybrid approach to explore new strategies that may result in more powerful solution methods. Experiments on randomly generated problems of practical complexity, the notorious 10×10 instance of Muth&Thompson's benchmark and several instances of Lawrence [22] show that the hybrid scheduler strategy produces good results, better than previous efforts using genetics algorithms, and comparable to existing search-based methods [2, 4, 21].

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Authors and Affiliations

  1. Facultad de Informática, Universidad del País Vasco, Plaza de Manuel de Lardizabal, 20009, San Sebastián, Spain

    José Ramón Zubizarreta Aizpuru & Javier Arrieta Usunáriz

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  1. José Ramón Zubizarreta Aizpuru
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  2. Javier Arrieta Usunáriz
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Carlos Pinto-Ferreira Nuno J. Mamede

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© 1995 Springer-Verlag Berlin Heidelberg

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Aizpuru, J.R.Z., Usunáriz, J.A. (1995). GA/TS: A hybrid approach for job shop scheduling in a production system. In: Pinto-Ferreira, C., Mamede, N.J. (eds) Progress in Artificial Intelligence. EPIA 1995. Lecture Notes in Computer Science, vol 990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60428-6_13

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  • DOI: https://doi.org/10.1007/3-540-60428-6_13

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  • Print ISBN: 978-3-540-60428-0

  • Online ISBN: 978-3-540-45595-0

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