Automated Planning for Liner Shipping Fleet Repositioning.
- Kevin Tierney,
- ,
- Christian Kroer,
- Adam Britt,
- Amanda Coles,
- Andrew Coles
- King’s College London,
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewPublication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 279-287 (8 pages)Publication milestones
- Published - 2012
Publication status
Published - 2012
Publisher
AAAI Press, United StatesISBN (Print)
ISBN 978-1-57735-562-5Publication IDs
- Scopus: 84866439111
Host publication title
ICAPS 2012, the 22nd International Conference on Automated Planning and Scheduling Abstract
The Liner Shipping Fleet Repositioning Problem (LSFRP) poses a large financial
burden on liner shipping firms. During repositioning, vessels are moved between
services in a liner shipping network. The LSFRP is characterized by chains of
interacting activities, many of which have costs that are a function of their
duration; for example, sailing slowly between two ports is cheaper than sailing
quickly. Despite its great industrial importance, the LSFRP has received
little attention in the literature. We show how the LSFRP can be solved
sub-optimally using the planner POPF and optimally with a mixed-integer
program (MIP) and a novel method called Temporal Optimization Planning (TOP).
We evaluate the performance of each of these techniques on a dataset of
real-world instances from our industrial collaborator, and show that automated
planning scales to the size of problems faced by industry.
burden on liner shipping firms. During repositioning, vessels are moved between
services in a liner shipping network. The LSFRP is characterized by chains of
interacting activities, many of which have costs that are a function of their
duration; for example, sailing slowly between two ports is cheaper than sailing
quickly. Despite its great industrial importance, the LSFRP has received
little attention in the literature. We show how the LSFRP can be solved
sub-optimally using the planner POPF and optimally with a mixed-integer
program (MIP) and a novel method called Temporal Optimization Planning (TOP).
We evaluate the performance of each of these techniques on a dataset of
real-world instances from our industrial collaborator, and show that automated
planning scales to the size of problems faced by industry.
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