Modelling the 2D object recognition task in manufacturing context: An information‐based model

In the last decays, manufacturing systems evolved to meet high product variety required by market. Different products can be manufactured in mixed-model assembly lines, with an increase process complexity. these production systems, flexibility is mainly provided operators final stages. Here, human errors could lead economic losses. A lack observed available research concerning a formal quantification of complexity considering joint effect shape and similarity mix variety. This paper focuses on operator decision-making 2D object recognition tasks, since this most critical task performed mixed model systems. novel quantify information content proposed. The based Shannon's Entropy theory considers both similarities. Numerical experiments are provided, results obtained show effectiveness capturing similarities content. proposed adopted environment for re-allocating tasks/sub-tasks avoid amount processed affecting operators' performance.