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Flow-shop scheduling by heuristic decomposition

Authors: Jatinder N. D. Gupta; A. R. Maykut;

Flow-shop scheduling by heuristic decomposition

Abstract

SUMMARY This paper investigates the classical n-job, M-machine flow-shop scheduling problem under the assumption that jobs are processed on all machines in the same order. Baaed on the heuristic job-pairing technique and the decomposition strategy, a heuristic decomposition algorithm is developed which will generate at least a near-optimal schedule for the flow-shop scheduling problem. The proposed algorithm is compared to the existing decomposition approach and is found to be superior to Ashour's decomposition algorithm, both in increased solution quality and decreased computational time required to solve the problem.

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    citations
    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).
    12
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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Found an issue? Give us feedback
citations
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!
12
Average
Top 10%
Average
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