Embedding Remaining Useful Life Predictions into a Modified Receding Horizon Task Assignment Algorithm to Solve Task Allocation Problems

Abstract

The Task Allocation problem is one of the fundamental combinatorial optimization problems with applications on various domains. Solving a Task Allocation problem consists in, given a set of tasks to be performed and a set of resources, defining which resource will perform each task in order to optimize an objective function. In this paper, we present a modified version of the Receding Horizon Task Assignment (RHTA) algorithm to solve multiple vehicle task assignment problems. In the proposed method, we generate a rejection list to reduce the number of candidate missions that are evaluated in each iteration of the RHTA algorithm. In addition, we incorporate in the mathematical formulation of the problem a set of constraints that limit the maximum mission duration that can be assigned to each vehicle. These constraints represent the predicted Remaining Useful Life (RUL) of each vehicle. Our model takes into account the execution time of each task and assumes that all vehicles must finish their missions at a base. The proposed model allows the vehicles to go to a base for maintenance during their missions. Numerical experiments are carried out using twenty benchmark problem instances. The results show that incorporating RUL predictions into task allocation problems increases the quality and the robustness of solutions.

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