Approximation algorithms for multi-agent scheduling to minimize total weighted completion time

We consider a multi-agent scheduling problem on a single machine in which each agent is responsible for his own set of jobs and wishes to minimize the total weighted completion time of his own set of jobs. It is known that the unweighted problem with two agents is NP-hard in the ordinary sense. For...

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Vydané v:Information processing letters Ročník 109; číslo 16; s. 913 - 917
Hlavní autori: Lee, Kangbok, Choi, Byung-Cheon, Leung, Joseph Y.-T., Pinedo, Michael L.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 31.07.2009
Elsevier
Elsevier Sequoia S.A
Predmet:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Approximation
Approximation algorithm
Approximations and expansions
Computer science; control theory; systems
Computer systems performance. Reliability
Exact sciences and technology
FPTAS
Mathematical analysis
Mathematics
Miscellaneous
Multi-agent scheduling
Multi-Objective Shortest-Path problem
Optimization
Production scheduling
Scheduling algorithms
Sciences and techniques of general use
Shortest path algorithms
Software
Studies
Theoretical computing
Total weighted completion time
Total weighted completion time
Multi-agent scheduling
Approximation algorithm
FPTAS
Multi-Objective Shortest-Path problem
Polynomial
Computer theory
Polynomial approximation
Shortest path
Scheduling
Polynomial time
Completion
Information processing
Algorithm analysis
Single machine
ISSN:0020-0190, 1872-6119
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Shrnutí:We consider a multi-agent scheduling problem on a single machine in which each agent is responsible for his own set of jobs and wishes to minimize the total weighted completion time of his own set of jobs. It is known that the unweighted problem with two agents is NP-hard in the ordinary sense. For this case, we can reduce our problem to a Multi-Objective Shortest-Path (MOSP) problem and this reduction leads to several results including Fully Polynomial Time Approximation Schemes (FPTAS). We also provide an efficient approximation algorithm with a reasonably good worst-case ratio.
Bibliografia:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0020-0190
1872-6119
DOI:10.1016/j.ipl.2009.04.018