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  <titleInfo>
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    <title>framework of more intelligent multicriteria decision support for selection and ranking</title>
  </titleInfo>
  <name type="personal">
    <namePart>Xiong, Dongwei</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Pastijn, Hugo</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Tabucanon, Mario T.</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Tang, John C.S.</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Asian Institute of Technology Partial</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>
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    </place>
    <place>
      <placeTerm type="text">Bangkok</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>1995</dateIssued>
    <issuance>continuing</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
    <extent>48 leaves</extent>
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  <abstract>In most of real word contexts, decisions must be made in presence of multiple, competing  and conflicting criteria. Such decision making problems fall down multicriteria decision  making (MCDM) domain.  This study aims to formulate a framework to solve discrete multicriteria decision making  problems by utilizing artificial intelligent decision support systems, and the method used in  this study is to integrate Decision Support System (DSS) and Expert System (ES)  technologies into MCDM. The major advantage of this approach is that all the data, such as  aggregation properties, MCDM methods, rules, and the DM's preferences and feelings, can be  integrated into a logical structure, which supports the decision maker (DM) in all aspects of  model building, and allows the DM to directly involve in MCDM processes.  The system consists of a user-friendly interface, a problem editor, a data translator, a  knowledge-based MCDM method choice sub-system, a review analysis sub-system and a  data base management system. The problem editor helps the DM to generate fully expended  alternatives, design the weights of criteria, and transform the descriptive data of multiattribute  problems, by rules, into a manageable decision matrix; the data translator aims to prepare the  data which are to be used by any MCDM method; the method choice sub-system allows the  DM to flexibly choose a suitable MCDM method among the model base and the sequence of  MCDM methods to solve decision problems; and the review analysis sub-system enables the  DM to make a comparative analysis of the results in order to gain new insights not only into  the problem, but also into the working of the methods as well as their adequacy to the types of  problems and DMs; the data base management system stores factual information and provides  data to MCDM models and the DM during the problem solving process.</abstract>
  <note>A research study submitted in partial fulfillment of the requirement for the degree of Master  of Engineering, School of Engineering and Technology</note>
  <note>Research Studies Project Report (M. Sc.) - Asian Institute of Technology, 1995</note>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Decision support systems</topic>
  </subject>
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    <titleInfo>
      <title>Research studies project report ; no. IE-95-1</title>
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      <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=B15467</identifier>
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