Citation
An approach to predictive-reactive scheduling of parallel machines subject to disruptions. Annals of Operations Research, 159 (1): 65-82, 2008.
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Abstract
In this paper, a new predictive-reactive approach to a parallel machine scheduling problem in the presence of uncertain disruptions is presented. The approach developed is based on generating a predictive schedule that absorbs the effects of possible uncertain disruptions through adding idle times to the job processing times. The uncertain disruption considered is material shortage, described by the number of disruption occurrences and disruption repair period. These parameters are specified imprecisely and modelled using fuzzy sets. If the impact of a disruption is too high to be absorbed by the predictive schedule, a rescheduling action is carried out. This approach has been applied to solving a real-life scheduling problem of a pottery company.
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Bibtex
@ARTICLE{2008-065-082-SI, author = {A. Duenas and D. Petrovic},
title = {An approach to predictive-reactive scheduling of parallel machines subject to disruptions},
journal = {Annals of Operations Research},
year = {2008},
volume = {159},
pages = {65--82},
number = {1},
note = {Selected},
abstract = {In this paper, a new predictive-reactive approach to a parallel machine scheduling problem in the presence of uncertain disruptions is presented. The approach developed is based on generating a predictive schedule that absorbs the effects of possible uncertain disruptions through adding idle times to the job processing times. The uncertain disruption considered is material shortage, described by the number of disruption occurrences and disruption repair period. These parameters are specified imprecisely and modelled using fuzzy sets. If the impact of a disruption is too high to be absorbed by the predictive schedule, a rescheduling action is carried out. This approach has been applied to solving a real-life scheduling problem of a pottery company.},
doi = {10.1007/s10479-007-0280-3},
owner = {user},
timestamp = {2012.05.25} }