<?xml version="1.0" encoding="UTF-8"?>
<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-1419</article-id>
      <title-group>
        <article-title>An inverse model for evaluating the power industry’s supply chain</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Pouralizadeh</surname>
            <given-names>Mojgan</given-names>
          </name>
          <aff>Department of Applied Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran</aff>
        </contrib>
      </contrib-group>
      <volume>47</volume>
      <issue>4</issue>
      <fpage>1313</fpage>
      <lpage>1334</lpage>
      <pub-date date-type="pub">
        <day>27</day>
        <month>02</month>
        <year>2025</year>
      </pub-date>
      <abstract>
        <p>In the clean development mechanism, greenhouse gas (GHG) reduction is conducted by implementing projects that aim to decrease fossil fuel consumption in the energy sectors, increase energy productivity, and mitigate energy loss in power plants and transmission networks and distribution systems. This; the Research evaluated the sustainability of the electrical supply chain using inverse input-oriented data envelopment analysis. The; proposed inverse input-oriented model estimates the optimal value of applied resources based on the optimal allocation of two categories of inputs while other production indexs and efficiency scores of supply chain divisions under evaluation remained unchanged. An empirical conclusion was yielded from the model’s performance in the ten electrical supply chains and 15 divisions. The gas field of supply chains, 60% utilize a large amount of gas resources in power production. Also; power plant sectors and transmitter lines of more than 90% of the supply chains produced an optimal value of energy. </p>
      </abstract>
      <kwd-group>
        <kwd>Optimal allocation</kwd>
        <kwd>Environmental efficiency</kwd>
        <kwd>Inverse model</kwd>
        <kwd>Optimal resources</kwd>
        <kwd>Economical return</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>
