  Multi-cycle assignment problems address scenarios where a series of general
assignment problems has to be solved sequentially. Subsequent cycles can differ
from previous ones due to changing availability or creation of tasks and
agents, which makes an upfront static schedule infeasible and introduces
uncertainty in the task-agent assignment process. We consider the setting
where, besides profit maximization, it is also desired to maintain diverse
assignments for tasks and agents, such that all tasks have been assigned to all
agents over subsequent cycles. This problem of multi-cycle assignment with
rotational diversity is approached in two sub-problems: The outer problem which
augments the original profit maximization objective with additional information
about the state of rotational diversity while the inner problem solves the
adjusted general assignment problem in a single execution of the model. We
discuss strategies to augment the profit values and evaluate them
experimentally. The method's efficacy is shown in three case studies:
multi-cycle variants of the multiple knapsack and the multiple subset sum
problems, and a real-world case study on the test case selection and assignment
problem from the software engineering domain.
