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<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-information-and-optimization-sciences</journal-id>
      <journal-title-group>
        <journal-title>Journal of Information and Optimization Sciences</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0103</issn>
      <issn publication-format="print">0252-2667</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JIOS-2489</article-id>
      <title-group>
        <article-title>Multi-objective optimization for emergency healthcare resource allocation</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Alate</surname>
            <given-names>Mahendra</given-names>
          </name>
          <aff>Directorate of Research, Krishna Vishwa Vidyapeeth (Deemed to be University), Karad, Maharashtra, 415539, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Wani</surname>
            <given-names>Tanveer Ahmad</given-names>
          </name>
          <aff>Department of Physics, Noida International University, Greater Noida, Uttar Pradesh, 201312, India</aff>
        </contrib>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Jadhav</surname>
            <given-names>Manisha Tushar</given-names>
          </name>
          <aff>Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Tejasvi</surname>
            <given-names>Koti</given-names>
          </name>
          <aff>Department of Computer Science and Engineering (Artificial Intelligence &amp; Machine Learning), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, 501218, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Anitha</surname>
            <given-names>K.</given-names>
          </name>
          <aff>Department of Management Studies, Meenakshi College of Arts &amp; Science, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, 600087, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Dahigaonkar</surname>
            <given-names>Dipak J.</given-names>
          </name>
          <aff>Department of Electronics and Communication Engineering, Ramdeobaba University, Nagpur, Maharashtra, 440013, India</aff>
        </contrib>
      </contrib-group>
      <volume>47</volume>
      <issue>10</issue>
      <fpage>3619</fpage>
      <lpage>3630</lpage>
      <pub-date date-type="pub">
        <day>31</day>
        <month>10</month>
        <year>2026</year>
      </pub-date>
      <abstract>
        <p>Emergency healthcare systems are faced with the challenging problems of distributing scarce resources quickly, fairly and economically and balancing limited resources with unpredictable demand. This study aims to present a multi-objective optimization methodology for ambulance allocation, healthcare manpower deployment, beds, equipments and emergency supplies to healthcare facilities. The model concurrently seeks to minimize response time, cost of operation, resource limitations and risk to the patient and maximize the scope of coverage and the utilization of resources. Forecasts of demand, facility levels, transportation limitations and priorities are included in the decision process. Balanced allocation alternatives of conflicting objectives are identified using Pareto based optimization. Simulation results show that better results in emergency coverage, waiting time, allocation cost and resource available have been achieved than that of traditional single objective approaches. The framework can be used to guide emergency healthcare planning in case of a routine crisis and/or a large-scale disaster that is responsive, transparent, and based on evidence.</p>
      </abstract>
      <kwd-group>
        <kwd>Emergency healthcare</kwd>
        <kwd>Resource allocation</kwd>
        <kwd>Multi-objective optimization</kwd>
        <kwd>Pareto optimization</kwd>
        <kwd>Response-time minimization</kwd>
        <kwd>Healthcare planning</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
