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Decomposition-Based Job-Shop Scheduling with Constrained Clustering

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Book cover Practical Aspects of Declarative Languages (PADL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 13165))

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Abstract

Scheduling is a crucial problem appearing in various domains, such as manufacturing, transportation, or healthcare, where the goal is to schedule given operations on available resources such that the operations are completed as soon as possible. Unfortunately, most scheduling problems cannot be solved efficiently, so that research on suitable approximation methods is of primary importance. This work introduces a novel approximation approach based on problem decomposition with data mining methodologies. We propose a constrained clustering algorithm to group operations into clusters, corresponding to time windows in which the operations must be scheduled. The decomposition process consists of two main phases. First, features are extracted, either from the problem itself or from solutions obtained by heuristic methods, to predict the execution sequence of operations on each resource. The second phase deploys our constrained clustering algorithm to assign each operation into a time window. We then schedule the operations by time windows using Answer Set Programming. Evaluation results show that our proposed approach outperforms other heuristic schedulers in most cases, where incorporating features like Remaining Processing Time, Machine Load, and Earliest Starting Time significantly improves the solution quality.

This work was partially funded by KWF project 28472, cms electronics GmbH, FunderMax GmbH, Hirsch Armbänder GmbH, incubed IT GmbH, Infineon Technologies Austria AG, Isovolta AG, Kostwein Holding GmbH, and Privatstiftung Kärntner Sparkasse.

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Notes

  1. 1.

    The benchmarks and our implementation are available at: https://github.com/Sa3doun13/PADL-2022.

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Author information

Authors and Affiliations

  1. Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Mohammed M. S. El-Kholany, Konstantin Schekotihin & Martin Gebser

  2. Technische Universität Graz, Graz, Austria

    Martin Gebser

  3. Cairo University, Cairo, Egypt

    Mohammed M. S. El-Kholany

Authors
  1. Mohammed M. S. El-Kholany
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  2. Konstantin Schekotihin
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  3. Martin Gebser
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Corresponding author

Correspondence to Martin Gebser .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    James Cheney

  2. University of Calabria, Rende, Italy

    Simona Perri

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El-Kholany, M.M.S., Schekotihin, K., Gebser, M. (2022). Decomposition-Based Job-Shop Scheduling with Constrained Clustering. In: Cheney, J., Perri, S. (eds) Practical Aspects of Declarative Languages. PADL 2022. Lecture Notes in Computer Science(), vol 13165. Springer, Cham. https://doi.org/10.1007/978-3-030-94479-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-94479-7_11

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