Development of a variance-based deterministic algorithm for stochastic MST in distribution networks
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| Název: | Development of a variance-based deterministic algorithm for stochastic MST in distribution networks |
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| Autoři: | Anna Angela Sitinjak, Saib Suwilo, Mardiningsih Mardiningsih, Sutarman Sutarman |
| Zdroj: | Eastern-European Journal of Enterprise Technologies; Vol. 3 No. 4 (135) (2025): Mathematics and Cybernetics-applied aspects; 42-51 Eastern-European Journal of Enterprise Technologies; Том 3 № 4 (135) (2025): Математика та кібернетика-прикладні аспекти; 42-51 |
| Informace o vydavateli: | Private Company Technology Center, 2025. |
| Rok vydání: | 2025 |
| Témata: | uncertainty modeling, spanning tree, stochastic graph, детерміністичне перетворення, стохастичний граф, variance-based algorithm, алгоритм на основі дисперсії, deterministic transformation, network optimization, кістякове дерево, моделювання невизначеності, оптимізація мережі |
| Popis: | This study addresses constructing Minimum Spanning Trees (MST) in stochastic weighted distribution networks, where edge costs have inherent uncertainties with known means and variances. Traditional deterministic methods often fail, and existing stochastic approaches are frequently unstable or computationally complex under high uncertainty. A novel variance-based deterministic transformation algorithm is proposed. Its core feature is transforming stochastic edge costs into robust deterministic equivalents by computing an aggregate variance term from the largest (n – 1) edge variances, enabling MST construction via classical algorithms. This method fundamentally enhances stability and ensures feasibility, particularly in high-variance scenarios, improving upon traditional confidence interval-based techniques. The algorithm’s efficacy was rigorously validated. Its performance was compared against a probabilistic Qij-based method under moderate variance, demonstrating consistent and accurate MSTs. It was then applied to a complex 21-edge distribution network with high variance parameters. Results confirm the algorithm’s broad applicability, precision, and capability to construct reliable spanning trees under both moderate and substantial uncertainty. The algorithm demonstrates significant computational efficiency (O(r log r)), ensuring practicality and scalability across varying uncertainty levels. Unlike iterative or constraint-heavy models, this algorithm simplifies optimization while preserving uncertainty representation. This makes it well-suited for large-scale networks and real-world systems where cost variability is critical. Future research includes expanding this approach to multi-objective optimization or dynamic networks |
| Druh dokumentu: | Article |
| Popis souboru: | application/pdf |
| ISSN: | 1729-4061 1729-3774 |
| DOI: | 10.15587/1729-4061.2025.329685 |
| Přístupová URL adresa: | https://journals.uran.ua/eejet/article/view/329685 |
| Rights: | CC BY |
| Přístupové číslo: | edsair.doi.dedup.....4a829f50ffa2a8ac48d7ae77a404fa17 |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This study addresses constructing Minimum Spanning Trees (MST) in stochastic weighted distribution networks, where edge costs have inherent uncertainties with known means and variances. Traditional deterministic methods often fail, and existing stochastic approaches are frequently unstable or computationally complex under high uncertainty. A novel variance-based deterministic transformation algorithm is proposed. Its core feature is transforming stochastic edge costs into robust deterministic equivalents by computing an aggregate variance term from the largest (n – 1) edge variances, enabling MST construction via classical algorithms. This method fundamentally enhances stability and ensures feasibility, particularly in high-variance scenarios, improving upon traditional confidence interval-based techniques. The algorithm’s efficacy was rigorously validated. Its performance was compared against a probabilistic Qij-based method under moderate variance, demonstrating consistent and accurate MSTs. It was then applied to a complex 21-edge distribution network with high variance parameters. Results confirm the algorithm’s broad applicability, precision, and capability to construct reliable spanning trees under both moderate and substantial uncertainty. The algorithm demonstrates significant computational efficiency (O(r log r)), ensuring practicality and scalability across varying uncertainty levels. Unlike iterative or constraint-heavy models, this algorithm simplifies optimization while preserving uncertainty representation. This makes it well-suited for large-scale networks and real-world systems where cost variability is critical. Future research includes expanding this approach to multi-objective optimization or dynamic networks |
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| ISSN: | 17294061 17293774 |
| DOI: | 10.15587/1729-4061.2025.329685 |