  As in-space exploration increases, autonomous systems will play a vital role
in building the necessary facilities to support exploration. To this end, an
autonomous system must be able to assign tasks in a scheme that efficiently
completes all of the jobs in the desired project. This research proposes a
flexible job shop problem (FJSP) representation to characterize an autonomous
assembly project and then proposes both a mixed integer programming (MIP)
solution formulation and a reinforcement learning (RL) solution formulation.
The MIP formulation encodes all of the constraints and interjob dynamics a
priori and was able to solve for the optimal solution to minimize the makespan.
The RL formulation did not converge to an optimal solution but did successfully
learn implicitly interjob dynamics through interaction with the reward
function. Future work will include developing a solution formulation that
utilizes the strengths of both proposed solution methods to handle scaling in
size and complexity.
