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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Rea Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>Rea Press</journal-title><issn pub-type="ppub">3115-932X</issn><issn pub-type="epub">3115-932X</issn><publisher>
      	<publisher-name>Rea Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.48314/anowa.v2i2.73</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Fractal-fractional calculus, Caputo-type derivative, Logistic growth, Soft computing, Fractional numerical methods, Memory effect</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>Caputo-Based Numerical Modeling of Fractional Logistic Growth with Fractal Structures</article-title><subtitle>Caputo-Based Numerical Modeling of Fractional Logistic Growth with Fractal Structures</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname>Akhavan Ghassabzade</surname>
		<given-names>Fahime </given-names>
	</name>
	<aff>Department of Mathematics, Faculty of Sciences, University of Gonabad, Gonabad, Iran.</aff>
	</contrib><contrib contrib-type="author">
	<name name-style="western">
	<surname>Bagherpoorfard </surname>
		<given-names>Mina </given-names>
	</name>
	<aff>Department of Mathematics, Fasa Branch, Islamic Azad University, Fasa, Iran.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>18</day>
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <volume>2</volume>
      <issue>2</issue>
      <permissions>
        <copyright-statement>© 2026 Rea Press</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>Caputo-Based Numerical Modeling of Fractional Logistic Growth with Fractal Structures</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			Transportation is one of the most important aspects of human activity, supporting various social and economic transactions. Meanwhile, in order to remain competitive, freight transportation businesses and logistics providers must deliver high-quality, dependable, and effective services. The effective design of the service network in this industry necessitates strategic and tactical decisions regarding service frequency, optimal route selection, and market share allocation among companies. In this study, we have examined the static competition between two transportation companies by utilizing a Mixed-Integer Nonlinear Programming (MINLP) model. The competition is studied by calculating entrant’s service frequency and each company’s market share using a logit function. In this type of competition, the incumbent’s decisions about route selection and frequency determination are known beforehand, and our goal is to maximize profits for the new market entrant. Additionally, a number of constraints have been taken into account, including route capacities, the maximum allowable frequency on each link, and penalty costs associated with the incomplete utilization of route capacities. To evaluate and validate the model, real-world data from the Iranian Road Maintenance and Transportation Organization has been employed. Furthermore, in the sensitivity analysis phase, the impacts of changing the values of important parameters on the model's outputs were evaluated. This investigation aims to improve understanding of the system's dynamics and clarify how these factors influence optimal decision-making processes. 
		</p>
		</abstract>
    </article-meta>
  </front>
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