Dynamic allocation of human resources: case study in the metal 4.0 manufacturing industry

Maude Beauchemin, Marc-André Ménard, Jonathan Gaudreault, Nadia Lehoux, Stéphane Agnard and Claude-Guy Quimper

International Journal of Production Research, 2023, vol. 61, issue 20, 6891-6907

Abstract: Industry 4.0 concepts make it possible to rethink human resources allocation, even for more traditional environments like metal machining. While parts machining on Computer Numerical Control (CNC) machines is automated, some manual tasks must still be executed by operators. The current approach is typically that operators are statically allocated to one or many machines. This causes avoidable bottlenecks. We propose an optimisation model to dynamically assign tasks to the operators with the objective of minimising production delays. Three different scenarios are compared; one representing the current widely used static allocation method and two others that allow more flexibility in the operators’ allocation. The dynamic task assignment problem is solved using a constraint programming model. The model was applied to a case study from a high-precision metal manufacturing job shop. Experimental results show that switching from a static allocation to a dynamic one reduces by 76% the average production delays caused by human operators. Supposing more versatile operators under the dynamic allocation leads to further improvements.

Date: 2023
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DOI: 10.1080/00207543.2022.2139002

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