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Theoretical Computer Science
Article . 2022 . Peer-reviewed
License: Elsevier TDM
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Article . 2022
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https://dx.doi.org/10.48550/ar...
Article . 2022
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A polynomial-time approximation scheme for parallel two-stage flowshops under makespan constraint

Authors: Weitian Tong; Yao Xu; Huili Zhang;

A polynomial-time approximation scheme for parallel two-stage flowshops under makespan constraint

Abstract

As a hybrid of the Parallel Two-stage Flowshop problem and the Multiple Knapsack problem, we investigate the scheduling of parallel two-stage flowshops under makespan constraint, which was motivated by applications in cloud computing and introduced by Chen et al. [3] recently. A set of two-stage jobs are selected and scheduled on parallel two-stage flowshops to achieve the maximum total profit while maintaining the given makespan constraint. We give a positive answer to an open question about its approximability proposed by Chen et al. [3]. More specifically, based on guessing strategies and rounding techniques for linear programs, we present a polynomial-time approximation scheme (PTAS) for the case when the number of flowshops is a fixed constant.

Theoretical Computer Science (2022)

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Keywords

FOS: Computer and information sciences, Combinatorial optimization, Deterministic scheduling theory in operations research, parallel two-stage flowshops, polynomial-time approximation scheme, multiple knapsacks, Approximation algorithms, makespan constraint, rounding, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Performance evaluation, queueing, and scheduling in the context of computer systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
Green