Demo Data from EDBT 2026 / reviewers in the wild / expert
Derek Soeder
dblp:15/6586
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
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
1 paper |
Mathematical optimization · 87% Approximation and online algorithms · 13% | |
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
task planning |
0.0 | 1 | 2003 | Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003 |
Mathematical optimization › scheduling
production scheduling |
0.0 | 1 | 2003 | Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003 |
Mathematical optimization
scheduling |
0.0 | 1 | 2003 | Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003 |
Approximation and online algorithms › approximation algorithms
approximation guarantees |
0.0 | 1 | 2003 | Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003 |
Methods — techniques the papers use, named apart from their topics
heuristics · 0.1SPT heuristic · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Task planning with transportation constraints: approximation bounds, implementation and experimentsabstractIn this paper we consider the problem of planning the execution of a set of tasks, where each task has associated some processing time and has to be transported to some destination. The problem arises in various applications in production planning and scheduling. We address several variants of the problem and discuss implementations of some of the proposed solutions. The objective is to compute a processing-and-delivery schedule so as to minimize the sum of delivery completion times. All the variants we consider are NP-hard. We experimentally evaluate the performance of some heuristics and show that the shortest processing time (SPT) heuristic consistently yields good schedules. We also briefly discuss results on scheduling with transportation in planar weighted subdivisions, that specifically address the computation of transportation times. Ovidiu Daescu, Derek Soeder, R. N. Uma |
ICRA | 2 |