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  <doc>
    <id>9019</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>45</pageFirst>
    <pageLast>57</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>184</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Search for K-best solutions in optimal design of energy supply systems by an extended MILP hierarchical branch and bound method</title>
    <abstract language="eng">For the purpose of making a decision in the optimal design of an energy supply system, it is important to investigate not only the optimal solution but also suboptimal ones which follow the optimal one without any omissions, what are called K-best solutions. In this paper, a mixed-integer linear programming method utilizing the hierarchical relationship between design and operation variables proposed previ- ously is extended to search the K-best solutions very efficiently. In addition, methods for updating the incumbents are incorporated into the extended method for three options for the criterion set newly in deriving the K-best solutions. This extended method is implemented into open and commercial MILP solvers, and is applied to illustrative and practical case studies, respectively, on the optimal design of cogeneration systems. Through the studies, it turns out that the proposed method is much superior in terms of solution optimality and computation efficiency to a conventional method, and that the computation efficiency to derive one of the K-best solutions by the proposed method increases with the number of K-best solutions. In addition, features of the K-best solutions in the value of objective function are clarified.</abstract>
    <parentTitle language="eng">Energy</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.energy.2018.02.077</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <author>Ryohei Yokoyama</author>
    <submitter>Yuji Shinano</submitter>
    <author>Yuji Shinano</author>
    <author>Syusuke Taniguchi</author>
    <author>Tetsuya Wakui</author>
    <collection role="persons" number="shinano">Shinano, Yuji</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
