  This paper proposes a novel integrated dynamic method based on Behavior Trees
for planning and allocating tasks in mixed human robot teams, suitable for
manufacturing environments. The Behavior Tree formulation allows encoding a
single job as a compound of different tasks with temporal and logic
constraints. In this way, instead of the well-studied offline centralized
optimization problem, the role allocation problem is solved with multiple
simplified online optimization sub-problem, without complex and cross-schedule
task dependencies. These sub-problems are defined as Mixed-Integer Linear
Programs, that, according to the worker-actions related costs and the workers'
availability, allocate the yet-to-execute tasks among the available workers. To
characterize the behavior of the developed method, we opted to perform
different simulation experiments in which the results of the action-worker
allocation and computational complexity are evaluated. The obtained results,
due to the nature of the algorithm and to the possibility of simulating the
agents' behavior, should describe well also how the algorithm performs in real
experiments.
