A Flexible Job Shop Scheduling Representation of the Autonomous In-Space Assembly Task Assignment Problem
This addresses task assignment for autonomous systems in space exploration, but it is incremental as it adapts existing scheduling methods to a new domain.
The paper tackles the problem of autonomous in-space assembly task assignment by proposing a flexible job shop scheduling representation, with a mixed integer programming solution achieving optimal makespan and a reinforcement learning solution learning interjob dynamics but not converging to optimality.
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.