We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search that is suitable for time critical applications. We created test instances on which we experimentally show that our approach outperforms a state-of-the-art approach to a related problem. Our approach improves the baseline's score on average by 2.35% and up to 10.53%, while responding in times shorter than the baseline's, on average, 1.6 s and up to 5.5 s shorter. Furthermore, our approach is robust to run replication and is not very sensitive to parameters tuning.
Bibliographical noteFunding Information:
Research supported: This work was supported by the Algerian Ministry of Higher Education and Scientific Research, scholarship number 187/PNE/USA/2013–2014.
© 2019 Walter de Gruyter GmbH, Berlin/Boston 2019.
- Multi-robot task allocation
- iterated local search