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Parallel Machine Scheduling withStochastic Workforce Skill Requirements

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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 632))

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Abstract

In the context of Industry 4.0, both of the ability to handle unexpected events and personalization customization are emphasized. This work investigates a parallel machine scheduling problem with uncertain skill requirements. The problem involves a two-stage decision-making process: (i) determining the workers’ skill training plan and the number of opened machines on the first stage before the realization of uncertain skill requirements, and (ii) scheduling jobs and assigning workers to jobs on the second stage, under known skill requirements. The objective is to minimize the expected total cost, including the workers’ skill training cost, machine opening cost and the expected penalty cost of jobs’ tardiness. A two-stage stochastic programming formulation is proposed, and an illustrative example shows the applicability of the model.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 72021002, 71771048, 71432007, 71832001 and 72071144.

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Authors and Affiliations

  1. Glorious Sun School of Business and Management, Donghua University, Shanghai, China

    Xin Liu

  2. School of Economics and Management, Tongji University, Shanghai, 200092, China

    Ming Liu

Authors
  1. Xin Liu
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  2. Ming Liu
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Correspondence to Ming Liu .

Editor information

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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Liu, X., Liu, M. (2021). Parallel Machine Scheduling withStochastic Workforce Skill Requirements. 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 632. Springer, Cham. https://doi.org/10.1007/978-3-030-85906-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-85906-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85905-3

  • Online ISBN: 978-3-030-85906-0

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