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Comparison of heuristic search algorithms for single machine scheduling problems

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Progress in Evolutionary Computation (EvoWorkshops 1993, EvoWorkshops 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 956))

Abstract

This paper compares the performance of four heuristic search algorithms for single machine scheduling problems: local search, simulated annealing, tabu search and genetic algorithms. To investigate their potential, the algorithms are applied to a single machine scheduling problem to minimise tardiness of all jobs with arbitrary ready times, processing times and due times. This problem is known to be NP complete. The purpose of the comparison is to find a good approximation algorithm ie. the algorithm is not designed to search for an optimal solution to the problem.

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Authors and Affiliations

  1. School of Information Technology, Bond University, 4229, Gold Coast, Qld, Australia

    Graham McMahon & Daniel Hadinoto

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  1. Graham McMahon
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  2. Daniel Hadinoto
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Xin Yao

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© 1995 Springer-Verlag Berlin Heidelberg

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McMahon, G., Hadinoto, D. (1995). Comparison of heuristic search algorithms for single machine scheduling problems. In: Yao, X. (eds) Progress in Evolutionary Computation. EvoWorkshops EvoWorkshops 1993 1994. Lecture Notes in Computer Science, vol 956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60154-6_62

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  • DOI: https://doi.org/10.1007/3-540-60154-6_62

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60154-8

  • Online ISBN: 978-3-540-49528-4

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