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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Sharif University of Technology</PublisherName>
				<JournalTitle>Scientia Iranica</JournalTitle>
				<Issn>1026-3098</Issn>
				<Volume>24</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2017</Year>
					<Month>06</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>A new proactive-reactive approach to hedge against uncertain processing times and unexpected machine failures in the two-machine flow shop scheduling problems</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1571</FirstPage>
			<LastPage>1584</LastPage>
			<ELocationID EIdType="pii">4136</ELocationID>
			
<ELocationID EIdType="doi">10.24200/sci.2017.4136</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>D.</FirstName>
					<LastName>Rahmani</LastName>
<Affiliation>Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2015</Year>
					<Month>10</Month>
					<Day>20</Day>
				</PubDate>
			</History>
		<Abstract>In this paper, a proactive-reactive approach has been considered for achieving stable and robust schedules despite uncertain processing times and unexpected machine failures in a two-machine  flow shop system. In the literature, Surrogate Measures (SMs) have been developed for achieving stable and robust solutions against the occurrence of stochastic disruptions. These measures proactively provide an approximation of the real conditions of the system in the event of a disruption. Because of the discrepancies of these measures with their real values, a dierent approach is developed in this paper in  two-step structure. First, an initial robust schedule is produced and then, based on a multi-component measure, an appropriate reaction is adopted against unexpected machine failures. Computational results indicate that this method produces better solutions compared to the other two classical scheduling approaches considering their eectiveness&lt;br /&gt;and performance.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">disruption</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">robustness</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">stability</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Nervousness</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Flow shop</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Proactive-reactive approach</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://scientiairanica.sharif.edu/article_4136_458f9edb40148e46e3b1b1bf7d7b51f2.pdf</ArchiveCopySource>
</Article>
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