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Theoretical Computer Science
Volume 361, Issues 2-3, 1 September 2006, Pages 257-274
Approximation and Online Algorithms
 
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doi:10.1016/j.tcs.2006.05.014    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier B.V. All rights reserved.

Open block scheduling in optical communication networks

A.A. Ageeva, 1, 4, E-mail The Corresponding Author, A.V. Fishkinb, 2, E-mail The Corresponding Author, A.V. Kononova, Corresponding Author Contact Information, 3, 4, E-mail The Corresponding Author and S.V. Sevastyanova, 4, E-mail The Corresponding Author

aSobolev Institute of Mathematics, Acad. Koptyug pr. 4, 630090 Novosibirsk, Russia bInstitut für Informatik und Praktische Mathematik, Christian-Albrechts-Universität zu Kiel, Olshausenstr. 40, 24 098 Kiel, Germany

Available online 9 June 2006.

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Abstract

In this paper the process of data transmission in star coupled optical communication networks is modelled as a shop-type scheduling problem, where channels (wavelengths) are treated as machines. We formulate an Open Block problem with the minimum makespan objective (OB||Cmax) in which a relation of a new type between the operations of each job is introduced: any two operations of a job have identical processing times and may be processed either simultaneously (in a common block) or, alternatively, at disjoint time intervals. We show that the problem is polynomially solvable for 4 machines, NP-hard for 6 machines and strongly NP-hard for a variable number of machines. For the case of a variable number of machines we present a polynomial time View the MathML source-approximation algorithm and prove that there is no polynomial time ρ-approximation algorithm with ρ<11/10, unless P=NP. For the case when the number of machines is fixed, we show that the problem admits a linear time PTAS. In addition, we show that the two-machine problem with release dates is NP-hard in the strong sense.

Keywords: Scheduling; Approximation; Open shop; Data packets


Theoretical Computer Science
Volume 361, Issues 2-3, 1 September 2006, Pages 257-274
Approximation and Online Algorithms
 
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