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  <titleInfo>
    <title>ART1 neural network for part machine grouping and cell formation</title>
    <subTitle>a case study</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Pratama, T. Iwan Budhi</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Pandey, P. C.</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Shanker, Kripa</namePart>
    <role>
      <roleTerm type="text">Examination committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Voratas Kachitvichyanukul</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Atma Jaya Yogyakarta University  Yogyakarta, Indonesia</namePart>
    <role>
      <roleTerm type="text">Scholarship donor</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">series</genre>
  <genre authority="marc">technical report</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">th</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Bangkok</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>1998</dateIssued>
    <issuance>continuing</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>62 p.</extent>
  </physicalDescription>
  <abstract>This thesis reports a case study of production system of Metals Workshop of PT INKA, a  rolling stock manufacturer in Indonesia. The objective of the study is to suggest ways to improve  the performance of the existing production system and layout based on the application of Binary  Adaptive Resonance Theory (ARTl) neural network and cell formation.  The ARTl algorithm is used to block-diagonalize the binary part-machine incidence  matrix. ARTl algorithm is applied twice. ARTl Column is used to classify the machines and  then ARTl Row is used to classify the parts. Since the solution by ARTl is not practical for this  data from a case study, then the extended procedure is generated to improve the ARTl  algorithm. The result is not only block-diagonalization of part-machine matrix, but also the  machine cell formation.  Two modifications for ARTl have been incorporated for improving the algorithm. The  presentation of input vectors in decreasing number of ls enables the ARTl to classify the input  vectors more consistently. Representing the bottom-up weight matrix in a vector form in  computer program has also been found to the capability of ART 1 by:  1. Reducing the computer memory requirements  2. Faster computation  Based on the part-machine grouping, manufacturing cells have been designed to  improved the machine utilization an reduction in machine and manpower requirements.  Arranging the existing machines into cells has led to substantial reduction in number of machines  required and corresponding reduction in manpower. This also has resulted into uniform  utilization of the manufacturing resources and avoids unnecessary duplication of manufacturing  resources. </abstract>
  <note>A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies</note>
  <note>Thesis (M.Sc.) - Asian Institute of Technology, 1998</note>
  <subject authority="lcsh">
    <topic>Group technology</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Neural networks (Computer science)</topic>
  </subject>
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    <titleInfo>
      <title>Thesis ; no. ISE-98-24</title>
    </titleInfo>
    <name type="corporate">
      <namePart>Asian Institute of Technology.</namePart>
      <namePart/>
    </name>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B13132</identifier>
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    <url displayLabel="Full-Text">http://203.159.5.9/ait-thesis/detail.php?q=B13132</url>
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    <recordCreationDate encoding="marc">140799</recordCreationDate>
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