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Optimizing the Solution Quality of Metaheuristics Through Process Mining Based on Selected Problems from Operations Research

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Business Process Management Forum (BPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 490))

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Abstract

Methods from Operations Research (OR) are employed to address a diverse set of Business Process Management (BPM) problems such as determining optimum resource allocation for process tasks. However, it has not been comprehensively investigated how BPM methods can be used for solving OR problems, although process mining, for example, provides powerful analytical instruments. Hence, in this work, we show how process discovery, a subclass of process mining, can generate problem knowledge to optimize the solutions of metaheuristics to solve a novel OR problem, i.e., the combined cobot assignment and job shop scheduling problem. This problem is relevant as cobots can cooperate with humans without the need for a safe zone and currently significantly impact transitions in production environments. In detail, we propose two process discovery based neighborhood operators, namely process discovery change and process discovery dictionary change, and implement and evaluate them in comparison with random and greedy operations based on a real-world data set. The approach is also applied to another OR problem for generalizability reasons. The combined OR and process discovery approach shows promising results, especially for larger problem instances.

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Notes

  1. 1.

    https://www.risc-software.at/.

  2. 2.

    https://w3.vdc.univie.ac.at/wiki/index.php/Slurm.

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Acknowledgments

This work has been partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 277991500. Additionally, we are thankful that the RISC Software GmbH allowed us to use the simulation framework, Easy4Sim.

Author information

Authors and Affiliations

  1. Forschungsverbund Data Science, University of Vienna, Kolingasse 14-16, 1090, Wien, Austria

    Alexander Kinast

  2. Department of Business Decisions and Analytics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090, Wien, Austria

    Roland Braune & Karl F. Doerner

  3. TUM School of Computation, Information, and Technology, Technical University of Munich, Boltzmannstrasse 3, 85748, Garching, Germany

    Stefanie Rinderle-Ma

Authors
  1. Alexander Kinast
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  4. Stefanie Rinderle-Ma
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Corresponding author

Correspondence to Alexander Kinast .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Chiara Di Francescomarino

  2. Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin

  3. TU Dortmund University, Dortmund, Germany

    Christian Janiesch

  4. The University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

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Kinast, A., Braune, R., Doerner, K.F., Rinderle-Ma, S. (2023). Optimizing the Solution Quality of Metaheuristics Through Process Mining Based on Selected Problems from Operations Research. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_14

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  • DOI: https://doi.org/10.1007/978-3-031-41623-1_14

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