Reactive Task Allocation for Balanced Servicing of Multiple Task Queues

Abstract

In this article, we propose a reactive task allocation architecture for a multi-agent system, for scenarios where the tasks arrive at random times and are grouped into multiple queues. Two-stage tasks are considered, where every task has a beginning, an intermediate and a final part, typical in pick-and-drop and inspect-and-report scenarios. A centralized auction-based task allocation system is proposed, where an auction system considers bids submitted by the agents for individual tasks, current length of the queues and the waiting times of the tasks in the queues, to decide on a task allocation strategy. The costs associated with these considerations, along with the constraints of having unique mappings between tasks and agents and constraints on the maximum number of agents that can be assigned to a queue, results in a Linear Integer Program (LIP) that is solved using the SCIP solver. For the scenario where the queue lengths are penalized but not the waiting times, we demonstrate that the auction system allocates tasks in a manner that all the queue lengths become constant, which is termed balancing. For the scenarios where both costs are considered, we qualitatively analyse the effect of the choice of the relative weights on the resulting task allocation and provide guidelines for the choice of the weights. We present simulation results that illustrate the balanced allocation of tasks and validate the analysis for the trade-off between the costs related to queue lengths and task waiting times.