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Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz)

KI 2016: KI 2016: Advances in Artificial Intelligence pp 141–147Cite as

A Multi-Objective Approach for both Makespan- and Energy-Efficient Scheduling in Injection Molding

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9904))

Abstract

Recent sustainability efforts require machine scheduling approaches to consider energy efficiency in the optimization of schedules. In this paper, an approach to reduce power peaks while maintaining the makespan is proposed and evaluated. The central concept of the approach is to slowly equalize highs and lows in the energy input of the schedule without affecting the makespan through an iterative optimization. The approach is based on the simulated annealing algorithm to optimize machine schedules regarding the makespan and the energy input, using the goal programming method as the objective function.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Oldenburg, Uhlhornsweg 84, 26129, Oldenburg, Germany

    Klaas Dählmann & Jürgen Sauer

Authors
  1. Klaas Dählmann
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  2. Jürgen Sauer
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Corresponding author

Correspondence to Klaas Dählmann .

Editor information

Editors and Affiliations

  1. Alpen-Adria Universität Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  2. University of Basel, Basel, Switzerland

    Malte Helmert

  3. Technische Universität Graz, Graz, Austria

    Franz Wotawa

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Dählmann, K., Sauer, J. (2016). A Multi-Objective Approach for both Makespan- and Energy-Efficient Scheduling in Injection Molding. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-46073-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46072-7

  • Online ISBN: 978-3-319-46073-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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