Demo Data from VLDB 2026 / reviewers in the wild / expert
Shinji Hasebe
dblp:42/5995
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2002
—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 · 100% | |
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
scheduling |
0.0 | 1 | 2002 | Machine-Oriented Decentralized Scheduling Method using Lagrangian Decomposition and Coordination Technique · ICRA 2002 |
Mathematical optimization
discrete optimization |
0.0 | 1 | 2002 | Machine-Oriented Decentralized Scheduling Method using Lagrangian Decomposition and Coordination Technique · ICRA 2002 |
Mathematical optimization › lagrangian relaxation
lagrangian decomposition |
0.0 | 1 | 2002 | Machine-Oriented Decentralized Scheduling Method using Lagrangian Decomposition and Coordination Technique · ICRA 2002 |
Methods — techniques the papers use, named apart from their topics
simulated annealing · 0.1lagrangian relaxation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | Machine-Oriented Decentralized Scheduling Method using Lagrangian Decomposition and Coordination TechniqueabstractIn the conventional Lagrangian relaxation approach, the scheduling problems are decomposed into job-level sub-problems or operation-level sub-problems. However, these approaches are not applicable to the flowshop problems with the changeover cost which depends on the sequence of operations. In this paper, we propose a machine-oriented decomposition method for the flowshop problems using the Lagrangian decomposition and coordination technique. In the proposed method, each sub-problem for single machine is solved by the simulated annealing method. The solutions of the sub-problems are used to generate a feasible schedule by a heuristic procedure. The effectiveness of the proposed method is verified by comparing the results of the example problems solved by the proposed method with those solved by the conventional method. Furthermore, it has been shown that the proposed approach is easily applicable to the flow hop problem with resource constrains minimizing the changeover costs and due date penalties. Tatsushi Nishi, Masami Konishi, Shinji Hasebe, Iori Hashimoto |
ICRA | 3 |