Abstract
In this paper, we formulate the material transportation problem as a multi-agent pickup and delivery with time synchronization (MAPD-TS) problem, which is an extension of the well-known multi-agent pickup and delivery (MAPD) problem. In MAPD-TS, we consider the synchronization of the movement of transportation agents with that of external agents, such as trucks arriving and departing from time to time in a warehouse and elevators that transfer materials to and from different floors in a construction site. We then propose methods via which agents autonomously select the tasks for improving overall efficiency by reducing unnecessary waiting times. MAPD is an abstract formation of material transportation tasks, and a number of methods have been proposed only for efficiency and collision-free movement in closed systems. However, as warehouses and construction sites are not isolated closed systems, transportation agents must sometimes synchronize with external agents to achieve real efficiency, and our MAPD-TS is the abstract form of this situation. In our proposed methods for MAPD-TS, agents approximately estimate their arrival time at the carry-in/out port connected with external agents and autonomously select the task to perform next for improved synchronization. Thereafter, we evaluate the performance of our methods by comparing them with the baseline algorithms. We demonstrate that our proposed algorithms reduce the waiting times of both agents and external agents and thus could improve overall efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andreychuk, A., Yakovlev, K., Surynek, P., Atzmon, D., Stern, R.: Multi-agent pathfinding with continuous time. Artif. Intell. 305, 103662 (2022). https://doi.org/10.1016/j.artint.2022.103662
Farinelli, A., Contini, A., Zorzi, D.: Decentralized task assignment for multi-item pickup and delivery in logistic scenarios. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1843–1845. IFAAMAS (2020)
Gong, X., Wang, T., Huang, T., Cui, Y.: Toward safe and efficient humanswarm collaboration: a hierarchical multi-agent pickup and delivery framework. IEEE Trans. Intell. Veh. 1–13 (2022). https://doi.org/10.1109/TIV.2022.3172342
Krakowczyk, D., Wolff, J., Ciobanu, A., Meyer, D.J., Hrabia, C.E.: Developing a distributed drone delivery system with a hybrid behavior planning system. In: German/Austrian Conference on Artificial Intelligence, pp. 107–114. Springer (2018). https://doi.org/10.1007/978-3-030-00111-7_10
Li, J., Tinka, A., Kiesel, S., Durham, J.W., Kumar, T.S., Koenig, S.: Lifelong multi-agent path finding in large-scale warehouses. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11272–11281 (2021)
Liu, M., Ma, H., Li, J., Koenig, S.: Task and path planning for multi-agent pickup and delivery. In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1152–1160. IFAAMAS (2019)
Ma, H., Li, J., Kumar, T., Koenig, S.: Lifelong multi-agent path finding for online pickup and delivery tasks. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 837–845. IFAAMAS (2017)
Ma, H., Tovey, C., Sharon, G., Kumar, T.S., Koenig, S.: Multi-agent path finding with payload transfers and the package-exchange robot-routing problem. In: The 30th AAAI Conference on Artificial Intelligence (2016). https://doi.org/10.1609/aaai.v30i1.10409
Nuutila, E., Soisalon-Soininen, E.: On finding the strongly connected components in a directed graph. Inf. Process. Lett. 49(1), 9–14 (1994). https://doi.org/10.1016/0020-0190(94)90047-7
Okumura, K., Machida, M., Défago, X., Tamura, Y.: Priority inheritance with backtracking for iterative multi-agent path finding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 535–542 (2019). https://doi.org/10.24963/ijcai.2019/76
Salzman, O., Stern, R.: Research challenges and opportunities in multi-agent path finding and multi-agent pickup and delivery problems. In: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1711–1715 (2020)
Sharon, G., Stern, R., Felner, A., Sturtevant, N.R.: Conflict-based search for optimal multi-agent pathfinding. Artif. Intell. 219, 40–66 (2015). https://doi.org/10.1016/j.artint.2014.11.006
Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972). https://doi.org/10.1137/0201010
Wu, X., et al.: Multi-agent pickup and delivery with task deadlines. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 360–367. Association for Computing Machinery (2021). https://doi.org/10.1145/3486622.3493915
Wurman, P.R., D’Andrea, R., Mountz, M.: Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Mag. 29(1), 9–20 (2008). https://doi.org/10.1609/aimag.v29i1.2082
Yamauchi, T., Miyashita, Y., Sugawara, T.: Path and action planning in non-uniform environments for multi-agent pickup and delivery tasks. In: European Conference on Multi-Agent Systems, pp. 37–54. Springer (2021). https://doi.org/10.1007/978-3-030-82254-5_3
Yamauchi, T., Miyashita, Y., Sugawara, T.: Standby-based deadlock avoidance method for multi-agent pickup and delivery tasks. In: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1427–1435. IFAAMAS (2022)
Yoshida, N., Noda, I., Sugawara, T.: Distributed service area control for ride sharing by using multi-agent deep reinforcement learning. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, pp. 101–112. INSTICC, SciTePress (2021). https://doi.org/10.5220/0010310901010112
Acknowledgements
This work was partly supported by JSPS KAKENHI Grant Number 20H04245.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yamauchi, T., Miyashita, Y., Sugawara, T. (2023). Task Selection Algorithm for Multi-Agent Pickup and Delivery with Time Synchronization. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-031-21203-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21202-4
Online ISBN: 978-3-031-21203-1
eBook Packages: Computer ScienceComputer Science (R0)