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        <identifier>oai:figshare.com:article/33993010</identifier>
        <datestamp>2026-09-25T05:31:18Z</datestamp>
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          <dc:title>Supplementary file 1_Multi-resource operating room scheduling: a three-stage heuristic for efficiency and patient safety.docx</dc:title>
          <dc:creator>Won-Chol Kim (25107613)</dc:creator>
          <dc:creator>So-Yong Kim (25107616)</dc:creator>
          <dc:creator>Gyong-Sim Ri (25107619)</dc:creator>
          <dc:creator>Hyon-U. Kong (25107622)</dc:creator>
          <dc:creator>Myong-Chol Chae (25107625)</dc:creator>
          <dc:creator>Un-Sung Pak (25107628)</dc:creator>
          <dc:subject>Operations Research</dc:subject>
          <dc:subject>emergency waiting time</dc:subject>
          <dc:subject>healthcare operations management</dc:subject>
          <dc:subject>heuristic algorithm</dc:subject>
          <dc:subject>multi-resource optimization</dc:subject>
          <dc:subject>operating room scheduling</dc:subject>
          <dc:subject>patient safety</dc:subject>
          <dc:subject>surgical throughput</dc:subject>
          <dc:description>Introduction&lt;p&gt;Operating room (OR) scheduling remains a critical healthcare operations challenge, yet existing approaches suffer from three fundamental limitations: they either oversimplify the multi-resource synchronization requirements, lack provable performance guarantees for clinical decision-making, or fail to adequately integrate emergency responsiveness with elective efficiency.&lt;/p&gt;Methods&lt;p&gt; To address these gaps, we formulate the multi-resource OR scheduling problem as a parallel machine scheduling problem with multi-machine joint processing and propose a three-stage heuristic algorithm (TSA) that achieves a worst-case 4/3 approximation ratio for makespan while integrating four objectives: minimizing makespan, reducing resource costs, maximizing high-priority patient satisfaction, and minimizing emergency waiting times.&lt;/p&gt;Results&lt;p&gt; Validated on 12,864 surgical records from the MIMIC-IV database, the TSA achieves a 15.79% increase in monthly surgical throughput, a 10.34% reduction in total resource costs, an 18.53% improvement in high-priority patient satisfaction, and a 50.00% reduction in critical surgery waiting time, all within 3.2 s for large-scale instances.&lt;/p&gt;Discussion&lt;p&gt; The TSA provides a computationally efficient, empirically validated decision support tool that bridges the gap between academic optimization theory and practical OR management requirements.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-25T05:31:18Z</dc:date>
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          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fieng.2026.1901669.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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