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Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Author

Listed:
  • Aitor Iriondo Pascual

    (Virtual Engineering Research Environment, School of Engineering Science, University of Skövde, 541 28 Skövde, Sweden)

  • Henrik Smedberg

    (Virtual Engineering Research Environment, School of Engineering Science, University of Skövde, 541 28 Skövde, Sweden)

  • Dan Högberg

    (Virtual Engineering Research Environment, School of Engineering Science, University of Skövde, 541 28 Skövde, Sweden)

  • Anna Syberfeldt

    (Virtual Engineering Research Environment, School of Engineering Science, University of Skövde, 541 28 Skövde, Sweden)

  • Dan Lämkull

    (Advanced Manufacturing Engineering, Volvo Car Corporation, 405 31 Göteborg, Sweden)

Abstract

Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.

Suggested Citation

  • Aitor Iriondo Pascual & Henrik Smedberg & Dan Högberg & Anna Syberfeldt & Dan Lämkull, 2022. "Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4894-:d:797085
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    References listed on IDEAS

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    1. Jack P. C. Kleijnen, 2009. "Factor Screening in Simulation Experiments: Review of Sequential Bifurcation," International Series in Operations Research & Management Science, in: Christos Alexopoulos & David Goldsman & James R. Wilson (ed.), Advancing the Frontiers of Simulation, pages 153-167, Springer.
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