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Instance Space Analysis for the Generalized Assignment Problem

  • Conference paper
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Metaheuristics (MIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13838))

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Abstract

In this work, we consider the well-studied Generalized Assignment Problem and investigate the performance of several metaheuristic methods. To obtain insights on strengths and weaknesses of these solution approaches, we perform Instance Space Analysis on the existing instance types and propose a set of features describing the hardness of an instance. This is of interest since the existing benchmark set is dated and rather limited and the known instance generators might not be fully representative. Our analysis for metaheuristic methods reveals that this is indeed the case and finds several gaps, which we fill with newly generated instances thus adding diversity and providing a new benchmark instance set. Furthermore, we analyze the impact of problem features on the performance of the methods used and identify the most important ones.

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Notes

  1. 1.

    https://dbai.tuwien.ac.at/user/tgeibing/gap/mic22.zip.

  2. 2.

    https://dbai.tuwien.ac.at/user/tgeibing/gap/instances/.

  3. 3.

    https://www.rust-lang.org/.

  4. 4.

    https://github.com/automl/SMAC3.

  5. 5.

    http://www.al.cm.is.nagoya-u.ac.jp/~yagiura/gap/.

  6. 6.

    https://github.com/andremun/InstanceSpace.

  7. 7.

    https://www.minizinc.org/challenge.html.

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Acknowledgments

This work has been funded by the Austrian security research programme KIRAS of the Federal Ministry of Agriculture, Regions and Tourism (BMLRT). Furthermore, the financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged.

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Authors and Affiliations

  1. Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling, TU Wien, Vienna, Austria

    Lucas Kletzander & Nysret Musliu

  2. Databases and Artificial Intelligence Group, Institute for Logic and Computation, TU Wien, Vienna, Austria

    Tobias Geibinger, Lucas Kletzander & Nysret Musliu

Authors
  1. Tobias Geibinger
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  2. Lucas Kletzander
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  3. Nysret Musliu
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Correspondence to Tobias Geibinger .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Luca Di Gaspero

  2. University of Napoli - Federico II, Napoli, Italy

    Paola Festa

  3. University of Paris Est, Paris, France

    Amir Nakib

  4. University of Catania, Catania, Italy

    Mario Pavone

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Geibinger, T., Kletzander, L., Musliu, N. (2023). Instance Space Analysis for the Generalized Assignment Problem. In: Di Gaspero, L., Festa, P., Nakib, A., Pavone, M. (eds) Metaheuristics. MIC 2022. Lecture Notes in Computer Science, vol 13838. Springer, Cham. https://doi.org/10.1007/978-3-031-26504-4_30

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  • DOI: https://doi.org/10.1007/978-3-031-26504-4_30

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  • Online ISBN: 978-3-031-26504-4

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