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<ArticleSet>
<Article>
<Journal>
				<PublisherName>Kharazmi University</PublisherName>
				<JournalTitle>International Journal of Supply and Operations Management</JournalTitle>
				<Issn>2383-1359</Issn>
				<Volume>1</Volume>
				<Issue>3</Issue>
				<PubDate PubStatus="epublish">
					<Year>2014</Year>
					<Month>11</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Mathematical Modelling for EOQ Inventory System with Advance Payment and Fuzzy Parameters</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>260</FirstPage>
			<LastPage>278</LastPage>
			<ELocationID EIdType="pii">2160</ELocationID>
			
<ELocationID EIdType="doi">10.22034/2014.3.01</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>S</FirstName>
					<LastName>Priyan</LastName>
<Affiliation>The Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, India</Affiliation>

</Author>
<Author>
					<FirstName>M.</FirstName>
					<LastName>Palanivel</LastName>
<Affiliation>The Gandhigram Rural Institute &amp;amp;ndash; Deemed University,
Gandhigram - 624 302, , India</Affiliation>

</Author>
<Author>
					<FirstName>R</FirstName>
					<LastName>Uthayakumar</LastName>
<Affiliation>The Gandhigram Rural Institute - Deemed University, Gandhigram - 624 302, India</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2014</Year>
					<Month>12</Month>
					<Day>01</Day>
				</PubDate>
			</History>
		<Abstract>This study considers an EOQ inventory model with advance payment policy in a fuzzy situation by employing two types of fuzzy numbers that are trapezoidal and triangular. Two fuzzy models are developed here. In the first model the cost parameters are fuzzified, but the demand rate is treated as crisp constant. In the second model, the demand rate is fuzzified but the cost parameters are treated as crisp constants. For each fuzzy model, we use signed distance method to defuzzify the fuzzy total cost and obtain an estimate of the total cost in the fuzzy sense. Numerical example is provided to ascertain the sensitiveness in the decision variables about fuzziness in the components. In practical situations, costs may be dependent on some foreign monetary unit. In such a case, due to a change in the exchange rates, the costs are often not known precisely. The first model can be used in this situation. In actual applications, demand is uncertain and must be predicted. Accordingly, the decision maker faces a fuzzy environment rather than a stochastic one in these cases. The second model can be used in this situation. Moreover, the proposed models can be expended for imperfect production process.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Inventory costs</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Advance payment</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Fuzzy numbers</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Signed distance method</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2160_1819932ff5cf474f4f19e7c7024640c2.pdf</ArchiveCopySource>
</Article>
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