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        <datestamp>2025-10-20</datestamp>
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          <dc:description>Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms</dc:description>
          <dc:description>The student, Sungi Hong, accepted the attached license on 2025-05-05 at 16:00.</dc:description>
          <dc:description>The student, Sungi Hong, submitted this Thesis for approval on 2025-05-05 at 16:26.</dc:description>
          <dc:description>This Thesis was approved for publication on 2025-05-07 at 13:44.</dc:description>
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          <dc:title>Maximum entropy principle approach for water distribution network optimization</dc:title>
          <dc:creator>Hong, Sungi</dc:creator>
          <dc:date>2025-05-07</dc:date>
          <dc:contributor>Beck, Carolyn</dc:contributor>
          <dc:subject>Water Distribution Network</dc:subject>
          <dc:subject>Optimization</dc:subject>
          <dc:subject>Deterministic Annealing, Maximum Entropy Principle</dc:subject>
          <dc:subject>Facility Location</dc:subject>
          <dc:language>eng</dc:language>
          <dc:description>We propose a deterministic annealing (DA) framework, based on Jaynes’ maximum entropy principle, for simultaneous joint optimization of reservoir placement, network routing, and associated transport costs in water distribution networks (WDNs). Our approach leverages DA to decentralize WDNs, addressing the vulnerabilities of traditional centralized systems and reducing energy costs. We introduce a capacity-constrained formulation that limits the number of demand points served by each intermediate source point, thereby preventing overload and enhancing system stability. To enforce the resulting inequality constraints, we derive update rules for secondary Lagrange multipliers and analyze the sensitivity of the parameters. Finally, we extend the framework to utilize real-world pipe networks and validate our algorithm through a case study on the Modena, Italy network. Experimental results demonstrate that our DA-based method achieves lower operational costs and improved resilience compared to previous approaches.</dc:description>
          <dc:date>2025-05</dc:date>
          <dc:type>Text</dc:type>
          <dc:identifier>https://hdl.handle.net/2142/129328</dc:identifier>
          <dc:rights>Copyright 2025 Sungi Hong</dc:rights>
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            <department>Industrial&amp;Enterprise Sys Eng</department>
            <discipline>Industrial Engineering</discipline>
            <grantor>University of Illinois Urbana-Champaign</grantor>
            <name>M.S.</name>
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