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Ageing workforce effects in Dual-Resource Constrained job-shop scheduling

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  • Berti, Nicola
  • Finco, Serena
  • Battaïa, Olga
  • Delorme, Xavier

Abstract

Currently, the ageing population is getting higher attention in production systems due to the increasing percentage of the ageing workforce, which persists longer in the working environment. Older workers are more vulnerable because of a possible decrease in physical capacities but can have a higher experience level. It is important to evaluate these factors during production process decisions and adapt the working environment to such workers. In such a way, production systems performance and workers’ well-being are jointly achieved. In companies aiming to include these aspects, digitalisation and automation can provide tools that can be used at tactical, strategic or operational levels. For example, at the operational level, wearable sensors, smart devices or trackers can be used in analysing the working time, effort and physical fatigue of operators via human-energy expenditure. These measures can be used to assess the required recovery time. However, the most challenging step is to include these measures in the decision-making process to organise the working process for operators according to their profiles and health restrictions. In this paper, we show how fatigue can be incorporated into operational processes such as scheduling in the context of a Dual-Resource Constrained (DRC) job shop. We consider several models to integrate rest allowance into the scheduling process and present a heuristic approach to assign operations to workers and machines. The impact of rest allowance on system performance is measured and managerial insights are provided.

Suggested Citation

  • Berti, Nicola & Finco, Serena & Battaïa, Olga & Delorme, Xavier, 2021. "Ageing workforce effects in Dual-Resource Constrained job-shop scheduling," International Journal of Production Economics, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:proeco:v:237:y:2021:i:c:s0925527321001274
    DOI: 10.1016/j.ijpe.2021.108151
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    References listed on IDEAS

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    Cited by:

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    2. Raja Awais Liaqait & Shermeen Hamid & Salman Sagheer Warsi & Azfar Khalid, 2021. "A Critical Analysis of Job Shop Scheduling in Context of Industry 4.0," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    3. Battini, Daria & Berti, Nicola & Finco, Serena & Zennaro, Ilenia & Das, Ajay, 2022. "Towards industry 5.0: A multi-objective job rotation model for an inclusive workforce," International Journal of Production Economics, Elsevier, vol. 250(C).
    4. Henao, César Augusto & Mercado, Yessica Andrea & González, Virginia I. & Lüer-Villagra, Armin, 2023. "Multiskilled personnel assignment with k-chaining considering the learning-forgetting phenomena," International Journal of Production Economics, Elsevier, vol. 265(C).
    5. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    6. Ranasinghe, Thilini & Senanayake, Chanaka D. & Grosse, Eric H., 2024. "Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system," International Journal of Production Economics, Elsevier, vol. 267(C).

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