Title: Simulation-based optimisation for worker cross-training

Authors: Johannes Karder; Andreas Beham; Viktoria A. Hauder; Klaus Altendorfer; Michael Affenzeller

Addresses: Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Production and Logistics Management, Johannes Kepler University, Linz, Austria ' Department of Operations Research, University of Applied Sciences Upper Austria, Steyr, Austria ' Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Hagenberg, Austria; Institute for Formal Models and Verification, Johannes Kepler University, Linz, Austria

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.

Keywords: worker cross-training; workforce qualification; encoding; multi-objective optimisation; NSGA-II; MOEA/D; simulation.

DOI: 10.1504/IJSPM.2021.117309

International Journal of Simulation and Process Modelling, 2021 Vol.16 No.3, pp.185 - 198

Received: 01 Jun 2020
Accepted: 08 Jan 2021
Published online: 31 Aug 2021 *

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