Multi-Agent Path Finding with Deadlines
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Abstract (arXiv)
We formalize Multi-Agent Path Finding with Deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within the deadline, without colliding with each other. We first show that MAPF-DL is NP-hard to solve optimally. We then present two classes of optimal algorithms, one based on a reduction of MAPF-DL to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network and the other one based on novel combinatorial search algorithms. Our empirical results demonstrate that these MAPF-DL solvers scale well and each one dominates the other ones in different scenarios.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 1.
7 papers.
- 2022Answer Set Planning: A Surveycited by 12
- 2022Graph-Based Multi-Robot Path Finding and Planningcited by 7
- 2022Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Findingcited by 5
- 2021A Competitive Analysis of Online Multi-Agent Path Findingcited by 2
- 2021DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologiescited by 1
- 2022Multi-Agent Path Finding via Tree LSTMcited by 1
- 2024Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Deliverycited by 0
- 2024Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery2 Mar 2024
- 2022Multi-Agent Path Finding via Tree LSTM24 Oct 2022
- 2022Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding2 Aug 2022
- 2022Graph-Based Multi-Robot Path Finding and Planning22 Jun 2022
- 2022Answer Set Planning: A Survey11 Feb 2022
- 2021DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies11 Nov 2021
- 2021A Competitive Analysis of Online Multi-Agent Path Finding22 Jun 2021