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We propose a multi-robot task-allocation strategy in which individuals' task assignments are carried out through the application of iterative permutations of agents' cost values that are associated with completing given tasks. Different possible permutations of agent-to-task assignments are calculated based on profile matrix that comprises all robotic agents' costs with regards to available tasks. The permutations that are calculated off-line are next used at runtime to dynamically and as per states of agents and available tasks ascertain the optimal agent-task allocations at every decision cycle. Proof of optimality of the adapted allocation strategy is presented. Performance of the proposed approach in multi-robot dynamic multi-task allocation scenario is demonstrated.