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    <name>Article</name>
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          <name>Title</name>
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              <text>Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands</text>
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          <name>Subject</name>
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              <text>Dynamic demands; Inventory routing; Quantum neural network; Snake artificial ecosystem optimization; Transshipments</text>
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              <text>The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP).  The Author(s) 2024.</text>
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              <text>Sivadevuni S.S.; J N.</text>
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              <text>International Journal of Networked and Distributed Computing, Vol-13, No. 1</text>
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              <text>Springer Science and Business Media B.V.</text>
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              <text>2025-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s44227-024-00048-7" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s44227-024-00048-7&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211185460&amp;amp;doi=10.1007%2Fs44227-024-00048-7&amp;amp;partnerID=40&amp;amp;md5=0096cc354e41a5f51be1c5dcea2b1551" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211185460&amp;amp;doi=10.1007%2fs44227-024-00048-7&amp;amp;partnerID=40&amp;amp;md5=0096cc354e41a5f51be1c5dcea2b1551&lt;/a&gt;</text>
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 22117938</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Sivadevuni S.S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560001, India; J N., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560001, India</text>
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