1. Incorporating the Human Factor within Manufacturing Dynamics
- Author
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F. Fruggiero, Stefano Riemma, Yassine Ouazene, V. Guglielmi, Roberto Macchiaroli, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), IFAC PAPERS ON LINE, Fruggiero, F., Riemma, S., Ouazene, Y., Macchiaroli, Roberto, and Guglielmi, V.
- Subjects
0209 industrial biotechnology ,Engineering ,Human Factors ,Multi Agent Systems ,Multi Agent System ,Chaotic ,02 engineering and technology ,020901 industrial engineering & automation ,Recovery rate ,Schema (psychology) ,0502 economics and business ,Tactical decision making ,Fatigue ,Simulation ,ComputingMilieux_MISCELLANEOUS ,Productivity ,Random assignment ,business.industry ,Multi-agent system ,05 social sciences ,Human Factor ,Dynamics Environment ,Control and Systems Engineering ,[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] ,Industrial engineering ,System dynamics ,Market behavior ,business ,050203 business & management - Abstract
This paper frames a model which summarizes the principal factors and relationships to incorporate human element, as per fatigue engagement, in the strategic and tactical decision making. A characterization of humans as per a dynamic interferer in system is presented. The findings are tested in an U-shaped assembly division of an international motor company. Dual-Resource Constrained sets are proposed. A Multi Agent model architecture, incorporating system dynamics modelling per humans, is implemented. ANOVA is executed for interaction analyses. Results designed optimal job sequence, rest break and task’s sequence, recovery rate and load characters as per enhanced system’s performances. Stable fatigued conditions are under complete recovery. Production rate in system is affected by fatigue that in turn increases with rise in switching rate. Chaotic market behavior prefers decentralized assignment of worker to task. Continuous switching suffers from learning attitude. Chaotic demand impairs fatigue across workforce reporting instable operator fatigue under different allocation rules. Under loudly physical tasks, random assignment is preferable for productivity outcomes. Autonomous control of workers at work considerably impairs fatigue under complete resting schema.
- Published
- 2016