TY - GEN
T1 - Reconfiguring Shortest Paths in Graphs
AU - Gajjar, Kshitij
AU - Jha, Agastya Vibhuti
AU - Kumar, Manish
AU - Lahiri, Abhiruk
N1 - Funding Information:
*This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 682203-ERC-[Inf-Speed-Tradeoff]. Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Funding Information:
Kshitij Gajjar's research at National University of Singapore is supported by an NUS ODPRT Grant, WBS No. R-252- 000-A94-133. A part of this work was also done when he was a Post-Doctoral Fellow at Technion, Israel. Agastya Vibhuti Jha would like to thank Dr. Jatin Batra for introducing him to Ordered Optimization, which gave the idea of k-SPR. Abhiruk Lahiri's research is supported by an Ariel University Post-Doctoral Fellowship, Israel Science Foundation, Grant Nos. 592/17 and 822/18, and the ERC-CZ project LL2005. He would like to thank the Caesarea Rothschild Institute and the Department of Computer Science, University of Haifa, for providing its facilities to carry out his research.
Funding Information:
Kshitij Gajjar’s research at National University of Singapore is supported by an NUS ODPRT Grant, WBS No. R-252-000-A94-133. A part of this work was also done when he was a Post-Doctoral Fellow at Technion, Israel.
Funding Information:
Abhiruk Lahiri’s research is supported by an Ariel University Post-Doctoral Fellowship, Israel Science Foundation, Grant Nos. 592/17 and 822/18, and the ERC-CZ project LL2005. He would like to thank the Caesarea Rothschild Institute and the Department of Computer Science, University of Haifa, for providing its facilities to carry out his research.
Publisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - Reconfiguring two shortest paths in a graph means modifying one shortest path to the other by changing one vertex at a time, so that all the intermediate paths are also shortest paths. This problem has several natural applications, namely: (a) revamping road networks, (b) rerouting data packets in a synchronous multiprocessing setting, (c) the shipping container stowage problem, and (d) the train marshalling problem. When modelled as graph problems, (a) is the most general case while (b), (c) and (d) are restrictions to different graph classes. We show that (a) is intractable, even for relaxed variants of the problem. For (b), (c) and (d), we present efficient algorithms to solve the respective problems. We also generalize the problem to when at most k (for some k ≥ 2) contiguous vertices on a shortest path can be changed at a time.
AB - Reconfiguring two shortest paths in a graph means modifying one shortest path to the other by changing one vertex at a time, so that all the intermediate paths are also shortest paths. This problem has several natural applications, namely: (a) revamping road networks, (b) rerouting data packets in a synchronous multiprocessing setting, (c) the shipping container stowage problem, and (d) the train marshalling problem. When modelled as graph problems, (a) is the most general case while (b), (c) and (d) are restrictions to different graph classes. We show that (a) is intractable, even for relaxed variants of the problem. For (b), (c) and (d), we present efficient algorithms to solve the respective problems. We also generalize the problem to when at most k (for some k ≥ 2) contiguous vertices on a shortest path can be changed at a time.
UR - http://www.scopus.com/inward/record.url?scp=85144181662&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85144181662
T3 - Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
SP - 9758
EP - 9766
BT - AAAI-22 Technical Tracks 9
PB - Association for the Advancement of Artificial Intelligence
T2 - 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Y2 - 22 February 2022 through 1 March 2022
ER -
