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Production Scheduling Using Multi-objective Optimization and Cluster Approaches

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Innovations in Bio-Inspired Computing and Applications (IBICA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 419))

Abstract

Production scheduling is a crucial task in the manufacturing process. In this way, the managers need to make decisions about the jobs production schedule. However, this task is not simple to perform, often requiring complex software tools and specialized algorithms to find the optimal solution. This work considers a multi-objective optimization algorithm to explore the production scheduling performance measure in order to help managers in decision making related to jobs attribution in a set of parallel machines. For this, five important production scheduling performance measures (makespan, tardiness and earliness time, number of tardy and early jobs) were combined into three objective functions and the Pareto front generated was analyzed by cluster techniques. The results presented different combinations to optimize the production process, providing to the manager different possibilities to prioritize the objectives considered.

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Acknowledgments

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, EXPL/EME-SIS/1224/2021.

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    Beatriz Flamia Azevedo & Ana I. Pereira

  2. Algoritmi Research Centre, University of Minho, Campus Azurém, 4800-058, Guimarães, Portugal

    Beatriz Flamia Azevedo, Maria Leonilde R. Varela & Ana I. Pereira

Authors
  1. Beatriz Flamia Azevedo
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  2. Maria Leonilde R. Varela
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  3. Ana I. Pereira
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Corresponding author

Correspondence to Beatriz Flamia Azevedo .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  3. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  5. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Anu Bajaj

  6. Faculty of Information Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  7. Vilnius University, Kaunas, Lithuania

    Dalia Kriksciuniene

  8. Lisbon University Institute, Lisbon, Portugal

    João Carlos Ferreira

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Azevedo, B.F., Varela, M.L.R., Pereira, A.I. (2022). Production Scheduling Using Multi-objective Optimization and Cluster Approaches. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_12

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