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Integrated Workforce Allocation and Scheduling in a Reconfigurable Manufacturing System Considering Cloud Manufacturing

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Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems (APMS 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 631))

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Abstract

The reconfigurable manufacturing system (RMS) has been acknowledged as an effective manufacturing paradigm to tackle high volatility in demand types and amounts. However, the reconfiguration needs an amount of time and leads to some level of resource wastage. Accordingly, a high frequency in the system’s reconfiguration may have a negative impact on its performance. In this regard, this paper investigates the advantage of using cloud manufacturing (CMfg) resources in enhancing the performance of an RMS system. A novel mathematical model is developed for the integrated workforce allocation and production scheduling problem utilizing the CMfg under a non-permutation flow shop setting. This model simultaneously makes decisions on the utilization of the CMfg capacity for performing some jobs, and for the remaining jobs, determination of machines’ configurations for each job, scheduling of the jobs on the machines, and allocation of operators to machines as well. This model aims to minimize the sum of job processing costs, overtime costs, and the cost of utilizing the CMfg resources. Finally, a computational experiment is conducted, which shows a promising improvement in the total cost of the production system by utilizing the CMfg capacity.

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Acknowledgment

This work was supported by the European Regional Development Fund under the project AI&Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15_003/0000466).

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Authors and Affiliations

  1. School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Behdin Vahedi-Nouri & Reza Tavakkoli-Moghaddam

  2. IlD-CIIRC, Czech Technical University in Prague, Prague, Czech Republic

    Zdenek Hanzalek

  3. IMT Atlantique, LS2N – CNRS, La Chantrerie, Nantes, France

    Alexandre Dolgui

Authors
  1. Behdin Vahedi-Nouri
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  2. Reza Tavakkoli-Moghaddam
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  3. Zdenek Hanzalek
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  4. Alexandre Dolgui
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Corresponding author

Correspondence to Behdin Vahedi-Nouri .

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Editors and Affiliations

  1. IMT Atlantique, Nantes, France

    Alexandre Dolgui

  2. Centrale Nantes, Nantes, France

    Alain Bernard

  3. IMT Atlantique, Nantes, France

    David Lemoine

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Vahedi-Nouri, B., Tavakkoli-Moghaddam, R., Hanzalek, Z., Dolgui, A. (2021). Integrated Workforce Allocation and Scheduling in a Reconfigurable Manufacturing System Considering Cloud Manufacturing. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-030-85902-2_57

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  • DOI: https://doi.org/10.1007/978-3-030-85902-2_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85901-5

  • Online ISBN: 978-3-030-85902-2

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