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A Dynamical Ant Colony Optimization with Heuristics for Scheduling Jobs on a Single Machine with a Common Due Date

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Metaheuristics for Scheduling in Industrial and Manufacturing Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 128))

Summary

The problem of scheduling jobs on a single machine with a common due date is one of NP-complete problems. It is to minimize the total earliness and tardiness penalties. This chapter introduces a Dynamical Ant Colony Optimization (DACO) with heuristics for scheduling jobs on a single machine with a common due date. In the proposed algorithm, the parameter of heuristic information is dynamically adjusted. Furthermore, additional heuristics are embedded into DACO as local search to escape from local optima. Compared with other existing approaches in the literature, the proposed algorithm is very useful for scheduling jobs on a single machine with a common due date.

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Author information

Authors and Affiliations

  1. Department of Information Management, Huafan University, No. 1, Huafan Rd. Shihding Township, Taipei County, 22301, Taiwan

    Zne-Jung Lee & Shih-Wei Lin

  2. Department of Industrial Engineering and Management Information, Huafan University, No. 1, Huafan Rd., Shihding Township, Taipei County, 22301, Taiwan

    Kuo-Ching Ying

Authors
  1. Zne-Jung Lee
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  2. Shih-Wei Lin
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  3. Kuo-Ching Ying
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Editor information

Editors and Affiliations

  1. Department of Languages and Informatics Systems, Polytechnic University of Catalonia, Campus Nord, Ed. Omega C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

  2. Norwegian Center of Excellence Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, NO-7491, Trondheim, Norway

    Ajith Abraham

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Lee, ZJ., Lin, SW., Ying, KC. (2008). A Dynamical Ant Colony Optimization with Heuristics for Scheduling Jobs on a Single Machine with a Common Due Date. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Studies in Computational Intelligence, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78985-7_4

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  • DOI: https://doi.org/10.1007/978-3-540-78985-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78984-0

  • Online ISBN: 978-3-540-78985-7

  • eBook Packages: EngineeringEngineering (R0)

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