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

AbstractIndustry 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.KEYWORDS: Industry 4.0job shop schedulinghuman resource allocationreal-time schedulingmetal parts machining AcknowledgementsThe authors wish to acknowledge APN Global for their implication in the project by contributing their time and data. A special thanks go to Keven Langlois for his time and knowledge, especially when extracting industrial data. The authors have obtained any necessary permissions for the reuse of this material "Beauchemin, Maude, "Dynamic Allocation of Operators in a Hybrid Human-Machine 4.0 Context" (2022). Theses and dissertations. https://corpus.ulaval.ca/entities/publication/cba35c96-5fdd-4465-8439-23f718f8ef45".Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementDue to the nature of this research, participants of this study did not agree to their data being shared publicly, so supporting data is not available.Additional informationFundingThis work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) [grant number 561168–20].Notes on contributorsMaude BeaucheminMaude Beauchemin is an M.Sc. candidate in the Department of Computer Science, Laval University, Canada. Her research interests include optimisation, simulation, operational research, industrial applications.Marc-André MénardMarc-André Ménard is a research professional associated with the CRISI, FORAC and IID. He obtained his PhD from Laval University in 2021. His research interests include artificial intelligence, optimisation, planning, scheduling and operational research.Jonathan GaudreaultJonathan Gaudreault is a professor in the Department of Computer Science, Laval University, Canada, and the director of the CRISI Research Consortium for Industry 4.0 Systems Engineering. His research interests include artificial intelligence-based decision support systems, planning and scheduling issues, operational research and optimisation, simulation, industrial applications.Nadia LehouxNadia Lehoux is a professor in Industrial Engineering at Laval University, Canada. Her research interests focus on operations research for logistics and operations planning problems.Stéphane AgnardStéphane Agnard is the R&D Director | Industry 4.0 at APN Global. He obtained his M.Eng. from the École de Technologie Supérieure University in 2013. Recipient of the Governor General's Academic Medal, he has contributed to many research projects over the years by always building bridges between the industrial and the academic worlds.Claude-Guy QuimperClaude-Guy Quimper is a professor in the Department of Computer Science, Laval University, Canada. He obtained his PhD from the University of Waterloo. His research interests include satisfaction and optimisation of combinatorial problems using constraint programming.