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A Multi-objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems

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Applications of Evolutionary Computation (EvoApplications 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12694))

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Abstract

Motivated by a real-world application, we consider an Assembly Job Shop Scheduling Problem (AJSSP), with three objectives: product quality, product quantity, and first product lead time. Using real-world inspection data, we demonstrate the ability to model product quality transformations during assembly jobs via genetic programming by considering the quality attributes of subparts. We investigate integrating quality transformation models into an AJSSP. Through the use of the de facto standard multi-objective evolutionary algorithm, NSGA-II, and a novel genotype to handle the constraints, we describe an evolutionary approach to optimizing all stated objectives. This approach is empirically shown to outperform random search and hill climbing in both performance and usability metrics expected to be valuable to administrators involved in plant scheduling and operations.

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Acknowledgements

This work is funded by the Department of Energy’s Kansas City National Security Campus, operated by Honeywell Federal Manufacturing & Technologies, LLC, under contract number DE-NA0002839.

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Authors and Affiliations

  1. BONSAI Lab, Auburn University, Auburn, AL, 36830, USA

    Michael H. Prince, Kristian DeHaan & Daniel R. Tauritz

Authors
  1. Michael H. Prince
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  2. Kristian DeHaan
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  3. Daniel R. Tauritz
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Corresponding authors

Correspondence to Michael H. Prince or Daniel R. Tauritz .

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Editors and Affiliations

  1. ETSIIT-CITIC, University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

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Prince, M.H., DeHaan, K., Tauritz, D.R. (2021). A Multi-objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems. In: Castillo, P.A., Jiménez Laredo, J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science(), vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_7

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  • DOI: https://doi.org/10.1007/978-3-030-72699-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72698-0

  • Online ISBN: 978-3-030-72699-7

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