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Derek Soeder

dblp:15/6586 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
task planning
0.012003
Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003
Mathematical optimization › scheduling
production scheduling
0.012003
Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003
Mathematical optimization
scheduling
0.012003
Task planning with transportation constraints: approximation bounds, implementation and experiments · ICRA 2003
Approximation and online algorithms › approximation algorithms
approximation guarantees
0.012003
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
YearPublicationVenuePosition
2003 Task planning with transportation constraints: approximation bounds, implementation and experiments
abstract
In 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
ICRA2