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  <titleInfo>
    <nonSort>An </nonSort>
    <title>adaptive particle swarm optimization algorithm for multiobjective job shop scheduling problems</title>
  </titleInfo>
  <name type="personal">
    <namePart>Suparat Wongnen</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Voratas Kachitvichyanukul</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Gong, Dah-Chuan</namePart>
    <role>
      <roleTerm type="text">Examination committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Huynh, Trung Luong</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Asian Institute of Technology Fellowship</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">Pathum Thani, Thailand</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2009</dateIssued>
    <issuance>continuing</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <extent>67 p. : ill.</extent>
  </physicalDescription>
  <abstract>This  research  introduces  an  adaptive  particle  swarm  optimization  algorithm  for  multi  objective  job  shop  scheduling  problems.  The  three  objectives  considered  in  this  research  are:  minimize  makespan,  minimize  total  weighted  earliness  and  minimize  total  weight  tardiness. The objective is to find optimal or near optimal results with these objectives. The solution  of  multi  objective  cases  will  be  represented  in  two  ways  as  weight  aggregating  approach and Pareto optimization approach. The proposed algorithm uses an adaptive PSO which is approach to solve the problem. The parameter  adaptation  of  PSO  is  focused  on  inertia  weight  and  acceleration  constants.  In  order to get high quality of solution the re-initialization strategy and local search and also applied in the proposed algorithm. The  proposed  algorithm  is  evaluated  by  using  the  benchmark  problems  provided  by  the  OR-library and compared it with best known results from published works for both single and multi objective cases. The proposed PSO algorithm can find good solutions in both of single  and  multi  objective  cases  in  term  of  solution  quality  and  computational  time.  However, the result from the proposed algorithm cannot performed better than 2ST-PSO in some cases because of the number of particles. Also, when the proposed algorithm use the same number of particles with 2ST-PSO, then the result performed better than 2ST-PSO in term of solution quality but take longer computational time.     </abstract>
  <note>Submitted in partial fulfillment of the requirements for the  degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering and Technology</note>
  <note>Thesis (M.Eng.) - Asian Institute of Technology, 2009</note>
  <subject authority="lcsh">
    <topic>Production scheduling</topic>
  </subject>
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    <titleInfo>
      <title>Thesis ; no. ISE-09-17</title>
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      <namePart>Asian Institute of Technology.</namePart>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B03259 </identifier>
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    <url displayLabel="Full-Text">http://203.159.5.9/ait-thesis/detail.php?q=B03259 </url>
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