Simulation-based optimisation for worker cross-training

Research output: Contribution to journalArticlepeer-review

Abstract

Worker cross-training is a problem arising in many companies that involve human work. To perform certain activities, workers are required to possess certain skills. Cross-trained workers possess even multiple skills, which enables a more flexible deployment, but also incurs higher costs. Thus, companies seek to balance the available skills such that customer deadlines can be met in a cost-efficient way. In this work we compare solution approaches for a simulation-based problem formulation with three objectives. We apply evolutionary multi-objective optimisation to a production system scenario with two lines and six workstations. Their performance is compared for a hard scenario where cross-training is essential to achieve high service levels. Results indicate that the algorithms are able to solve this three-objective formulation quite well using the described encoding and operators. Employing this technology at companies could lead to better qualification strategies and a better contribution of qualification efforts to company goals.

Original languageEnglish
Pages (from-to)185-194
Number of pages10
JournalInternational Journal of Simulation and Process Modelling
Volume16
Issue number3
Early online date2021
DOIs
Publication statusPublished - 2021

Keywords

  • Encoding
  • MOEA/D
  • Multi-objective optimisation
  • NSGA-II
  • Simulation
  • Worker cross-training
  • Workforce qualification

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