| Title: | Improved Whale Optimization Algorithm for supply chain financial risk assessment of cloud warehouse platform |
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| Authors: | ID Zhang, H. (Author)
ID Guo, Y. W. (Author)
ID Hou, Y. (Author)
ID Tang, L. (Author)
ID Deveci, Muhammet (Author) |
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| Files: |  APEM19-3_395-407.pdf (630,28 KB)
MD5: 418D80598FC1D7D2E5F1C7FB7105EBBF
 https://apem-journal.org/Archives/2024/Abstract-APEM19-3_395-407.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | This study provides an in-depth analysis of a new financial model for cloud warehouses and evaluates the associated credit risk within the context of supply chain financing, focusing on the intelligent transformation in this field. Concurrently, an optimization problem was derived from the evaluation issue, with the whale optimization algorithm (WOA) used to identify a reasonable default point and distance. To simplify the identification of these points, we enhanced the traditional WOA, resulting in an improved version, the IWOA, which demonstrated very good optimization performance. The IWOA's optimization capabilities were applied to determine the optimal ratio of short- and long-term debt coefficients, identifying the default point in the Kealhofer, McQuown, and Vasicek (KMV) credit monitoring model, replacing fixed values and yielding more precise results. Furthermore, this study introduces a novel analytical approach to credit risk measurement, advancing the development of related theories and methods. Accurate analysis of financial stability and risk is crucial in industrial sectors, including engineering and manufacturing. The simulation using specific data revealed that the IWOA-KMV model exhibited better and faster optimization capabilities, with greater discrimination ability compared to the KMV model. Overall, this study examines the risk factors in the cloud warehouse financing model, offers an improved version of the WOA, introduces a modified IWOA-KMV model to create a scientific, practical credit risk assessment framework, and provides guidance for risk control in cloud warehouse financing, a novel financing service. |
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| Keywords: | cloud warehouse platform, supply chain finance, risk assessment, KMV model, swarm intelligence, improved Whale Optimization Algorithm |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 19.09.2024 |
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| Article acceptance date: | 15.10.2024 |
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| Publication date: | 31.10.2024 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 395-407 |
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| Numbering: | Vol. 19, no. 3 |
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| PID: | 20.500.12556/DKUM-96931  |
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| UDC: | 004.8 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 267065603 |
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| DOI: | 10.14743/apem2024.3.515 |
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| Copyright: | Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
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| Publication date in DKUM: | 03.02.2026 |
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| Views: | 135 |
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| Downloads: | 3 |
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| Metadata: |    |
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| Categories: | Misc.
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