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Julia Romanski

dblp:178/8637 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Mathematical optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Smart cities and intelligent transportation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › large-scale optimization › decomposition methods
benders decomposition
0.522016
Benders Decomposition for Large-Scale Prescriptive Evacuations · AAAI 2016
Optimizing Infrastructure Enhancements for Evacuation Planning · AAAI 2016
Mathematical optimization
discrete optimization
0.212016
Benders Decomposition for Large-Scale Prescriptive Evacuations · AAAI 2016
Mathematical optimization › discrete optimization
mixed integer linear programming
0.212016
Optimizing Infrastructure Enhancements for Evacuation Planning · AAAI 2016
Smart cities and intelligent transportation › disaster management
evacuation planning
0.122016
Benders Decomposition for Large-Scale Prescriptive Evacuations · AAAI 2016
Optimizing Infrastructure Enhancements for Evacuation Planning · AAAI 2016

Methods — techniques the papers use, named apart from their topics

benders decomposition · 1.0mixed-integer programming · 0.5
YearPublicationVenuePosition
2016 Optimizing Infrastructure Enhancements for Evacuation Planning
abstract
With rapid population growth and urbanization, emergency services in various cities around the world worry that the current transportation infrastructure is no longer adequate for large-scale evacuations. This paper considers how to mitigate this issue through infrastructure upgrades, such as the additions of lanes to road segments and the raising of bridges and roads. The paper proposes a MIP model for deciding the most effective infrastructure upgrades as well as a Benders decomposition approach where the master problem jointly plans the upgrades and evacuation routes and the subproblem schedules the evacuation itself. Experimental results demonstrate the practicability of the approach on a real case study, filling a significant need for emergencies services.
Kanal Kumar, Julia Romanski, Pascal Van Hentenryck
AAAI2
2016 Benders Decomposition for Large-Scale Prescriptive Evacuations
abstract
This paper considers prescriptive evacuation planning for a region threatened by a natural disaster such a flood, a wildfire, or a hurricane. It proposes a Benders decomposition that generalizes the two-stage approach proposed in earlier work for convergent evacuation plans. Experimental results show that Benders decomposition provides significant improvements in solution quality in reasonable time: It finds provably optimal solutions to scenarios considered in prior work, closing these instances, and increases the number of evacuees by 10 to 15% on average on more complex flood scenarios.
Julia Romanski, Pascal Van Hentenryck
AAAI1