Modeling and simulation of human behavior impact on production throughput

The development of new technologies generates intelligent, complex, and collaborative production systems. Several research works want to improve the production performances while improving the comfort of the human operator. However, it is not obvious to define optimal strategies of operations planning and control that consider the unexpected and variable character of human operators. It is necessary to understand and model human behavior to develop predictive and dynamic actions. Even if some generic behaviors have been well integrated into classical quasi-deterministic models, there is still a need to develop stochastic models even closer to human behavior to allow more dynamic and anticipatory decision-making, especially at the operational level. In this work, we propose to model human behavior by a Markov chain and to evaluate the effect of the different behavior types on the production system performance. A heterogeneous set of human operators, with different behavioral patterns, were generated and tested through simulation. Earlier results demonstrate that there is a direct link between the behavior of human operators and the performance of the production system. It demonstrates also how to integrate such models in a dynamic decision-making process concerning, the assignment of workers to workstations.