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A matheuristic for workforce planning with employee learning and stochastic demand

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  • Silviya Valeva
  • Mike Hewitt
  • Barrett W. Thomas

Abstract

This paper focuses on the opportunity to direct the development of responsive capacity by recognising that individuals learn through experience when designing workforce plans. We focus on the operations of a product manufacturer that seeks to maximise profit by selling multiple products, while recognising that demands for each product is uncertain. As such, we study a stochastic integer program wherein an organisation can hedge against uncertainty in demand both by holding inventory (at a cost) and building a more responsive production process. Solving this stochastic program presents many computational difficulties, including the fact that quantitative models of human learning are non-linear and the explosion of instance size that result from modelling uncertainty with scenarios. As a result, we propose a matheuristic for this problem and with an extensive computational study demonstrate its ability to produce high-quality solutions in little time.

Suggested Citation

  • Silviya Valeva & Mike Hewitt & Barrett W. Thomas, 2017. "A matheuristic for workforce planning with employee learning and stochastic demand," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7380-7397, December.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:24:p:7380-7397
    DOI: 10.1080/00207543.2017.1349950
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    Cited by:

    1. Ulmer, Marlin & Nowak, Maciek & Mattfeld, Dirk & Kaminski, BogumiƂ, 2020. "Binary driver-customer familiarity in service routing," European Journal of Operational Research, Elsevier, vol. 286(2), pages 477-493.
    2. Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    3. Douglas S. Altner & Erica K. Mason & Les D. Servi, 2019. "Two-stage stochastic days-off scheduling of multi-skilled analysts with training options," Journal of Combinatorial Optimization, Springer, vol. 38(1), pages 111-129, July.
    4. Li, Yifu & Zhou, Chenhao & Yuan, Peixue & Ngo, Thi Tu Anh, 2023. "Experience-based territory planning and driver assignment with predicted demand and driver present condition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).

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